Positional Arguments

• src1: UMat.
• src2: UMat.

Keyword Arguments

• dst: UMat.

Calculates the per-element absolute difference between two arrays or between an array and a scalar. The function cv::absdiff calculates

Positional Arguments

• src1: UMat.
• src2: UMat.

Keyword Arguments

• dst: UMat.

Calculates the per-element absolute difference between two arrays or between an array and a scalar. The function cv::absdiff calculates

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• mask: UMat.

Adds an image to the accumulator image. The function adds src or some of its elements to dst : \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] The function supports multi-channel images. Each channel is processed independently. The function cv::accumulate can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation. @sa accumulateSquare, accumulateProduct, accumulateWeighted

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• mask: UMat.

Adds an image to the accumulator image. The function adds src or some of its elements to dst : \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] The function supports multi-channel images. Each channel is processed independently. The function cv::accumulate can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation. @sa accumulateSquare, accumulateProduct, accumulateWeighted

Positional Arguments

• src1: UMat.
• src2: UMat.
• dst: UMat.

Keyword Arguments

• mask: UMat.

Adds the per-element product of two input images to the accumulator image. The function adds the product of two images or their selected regions to the accumulator dst : \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] The function supports multi-channel images. Each channel is processed independently.

Positional Arguments

• src1: UMat.
• src2: UMat.
• dst: UMat.

Keyword Arguments

• mask: UMat.

Adds the per-element product of two input images to the accumulator image. The function adds the product of two images or their selected regions to the accumulator dst : \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] The function supports multi-channel images. Each channel is processed independently.

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• mask: UMat.

Adds the square of a source image to the accumulator image. The function adds the input image src or its selected region, raised to a power of 2, to the accumulator dst : \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] The function supports multi-channel images. Each channel is processed independently.

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• mask: UMat.

Adds the square of a source image to the accumulator image. The function adds the input image src or its selected region, raised to a power of 2, to the accumulator dst : \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] The function supports multi-channel images. Each channel is processed independently.

Positional Arguments

• src: UMat.
• dst: UMat.
• alpha: double.

Keyword Arguments

• mask: UMat.

Updates a running average. The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence: \f[\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multi-channel images. Each channel is processed independently.

Positional Arguments

• src: UMat.
• dst: UMat.
• alpha: double.

Keyword Arguments

• mask: UMat.

Updates a running average. The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence: \f[\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multi-channel images. Each channel is processed independently.

Positional Arguments

• src: UMat.
• maxValue: double.
• adaptiveMethod: int.
• thresholdType: int.
• blockSize: int.
• c: double.

Keyword Arguments

• dst: UMat.

Applies an adaptive threshold to an array. The function transforms a grayscale image to a binary image according to the formulae

Positional Arguments

• src: UMat.
• maxValue: double.
• adaptiveMethod: int.
• thresholdType: int.
• blockSize: int.
• c: double.

Keyword Arguments

• dst: UMat.

Applies an adaptive threshold to an array. The function transforms a grayscale image to a binary image according to the formulae

Positional Arguments

• src1: UMat.
• src2: UMat.

Keyword Arguments

• dst: UMat.
• mask: UMat.
• dtype: int.

Calculates the per-element sum of two arrays or an array and a scalar. The function add calculates

Positional Arguments

• src1: UMat.
• src2: UMat.

Keyword Arguments

• dst: UMat.
• mask: UMat.
• dtype: int.

Calculates the per-element sum of two arrays or an array and a scalar. The function add calculates

Positional Arguments

• img: Mat.
• text: String.
• org: Point.
• nameFont: String.

Keyword Arguments

• pointSize: int.
• color: Scalar.
• weight: int.
• style: int.
• spacing: int.

Draws a text on the image.

Positional Arguments

• img: Mat.
• text: String.
• org: Point.
• nameFont: String.

Keyword Arguments

• pointSize: int.
• color: Scalar.
• weight: int.
• style: int.
• spacing: int.

Draws a text on the image.

Positional Arguments

• src1: UMat.
• alpha: double.
• src2: UMat.
• beta: double.
• gamma: double.

Keyword Arguments

• dst: UMat.
• dtype: int.

Calculates the weighted sum of two arrays. The function addWeighted calculates the weighted sum of two arrays as follows: \f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently. The function can be replaced with a matrix expression

Positional Arguments

• src1: UMat.
• alpha: double.
• src2: UMat.
• beta: double.
• gamma: double.

Keyword Arguments

• dst: UMat.
• dtype: int.

Calculates the weighted sum of two arrays. The function addWeighted calculates the weighted sum of two arrays as follows: \f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently. The function can be replaced with a matrix expression

Variaint 1:

Positional Arguments

• src: UMat.
• userColor: UMat.

Keyword Arguments

• dst: UMat.

Applies a user colormap on a given image.

Variaint 1:

Positional Arguments

• src: UMat.
• userColor: UMat.

Keyword Arguments

• dst: UMat.

Applies a user colormap on a given image.

Positional Arguments

• curve: UMat.
• epsilon: double.
• closed: bool.

Keyword Arguments

• approxCurve: UMat.

Approximates a polygonal curve(s) with the specified precision. The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm

Positional Arguments

• curve: UMat.
• epsilon: double.
• closed: bool.

Keyword Arguments

• approxCurve: UMat.

Approximates a polygonal curve(s) with the specified precision. The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm

Calculates a contour perimeter or a curve length. The function computes a curve length or a closed contour perimeter.

Positional Arguments

• img: UMat. Image.
• pt1: Point.
• pt2: Point.
• color: Scalar.

Keyword Arguments

• thickness: int.
• line_type: int.
• shift: int.
• tipLength: double.

Draws an arrow segment pointing from the first point to the second one. The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.

Positional Arguments

• img: UMat. Image.
• pt1: Point.
• pt2: Point.
• color: Scalar.

Keyword Arguments

• thickness: int.
• line_type: int.
• shift: int.
• tipLength: double.

Draws an arrow segment pointing from the first point to the second one. The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.

Positional Arguments

• src1: UMat
• src2: UMat
• dtype: int

Keyword Arguments

• dist: UMat.
• nidx: UMat.
• normType: int.
• k: int.
• mask: UMat.
• update: int.
• crosscheck: bool.

naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search @todo document

Positional Arguments

• src1: UMat
• src2: UMat
• dtype: int

Keyword Arguments

• dist: UMat.
• nidx: UMat.
• normType: int.
• k: int.
• mask: UMat.
• update: int.
• crosscheck: bool.

naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search @todo document

Positional Arguments

• src: UMat.
• d: int.
• sigmaColor: double.
• sigmaSpace: double.

Keyword Arguments

• dst: UMat.
• borderType: int.

Applies the bilateral filter to an image. The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters. Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish". Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering. This filter does not work inplace.

Positional Arguments

• src: UMat.
• d: int.
• sigmaColor: double.
• sigmaSpace: double.

Keyword Arguments

• dst: UMat.
• borderType: int.

Applies the bilateral filter to an image. The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters. Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish". Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering. This filter does not work inplace.

Positional Arguments

• src1: UMat
• src2: UMat
• weights1: UMat
• weights2: UMat

Keyword Arguments

• dst: UMat.

Has overloading in C++

Positional Arguments

• src1: UMat
• src2: UMat
• weights1: UMat
• weights2: UMat

Keyword Arguments

• dst: UMat.

Has overloading in C++

Positional Arguments

• src: UMat.
• ksize: Size.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• borderType: int.

Blurs an image using the normalized box filter. The function smooths an image using the kernel: \f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] The call blur(src, dst, ksize, anchor, borderType) is equivalent to boxFilter(src, dst, src.type(), ksize, anchor, true, borderType).

Positional Arguments

• src: UMat.
• ksize: Size.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• borderType: int.

Blurs an image using the normalized box filter. The function smooths an image using the kernel: \f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] The call blur(src, dst, ksize, anchor, borderType) is equivalent to boxFilter(src, dst, src.type(), ksize, anchor, true, borderType).

Computes the source location of an extrapolated pixel. The function computes and returns the coordinate of a donor pixel corresponding to the specified extrapolated pixel when using the specified extrapolation border mode. For example, if you use cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it looks like

Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. The function calculates and returns the minimal up-right bounding rectangle for the specified point set or non-zero pixels of gray-scale image.

Positional Arguments

• src: UMat.
• ddepth: int.
• ksize: Size.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• normalize: bool.
• borderType: int.

Blurs an image using the box filter. The function smooths an image using the kernel: \f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] where \f[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\f] Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.

Positional Arguments

• src: UMat.
• ddepth: int.
• ksize: Size.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• normalize: bool.
• borderType: int.

Blurs an image using the box filter. The function smooths an image using the kernel: \f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] where \f[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\f] Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.

Positional Arguments

• box: RotatedRect.

Keyword Arguments

• points: UMat.

Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. The function finds the four vertices of a rotated rectangle. This function is useful to draw the rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information.

Positional Arguments

• box: RotatedRect.

Keyword Arguments

• points: UMat.

Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. The function finds the four vertices of a rotated rectangle. This function is useful to draw the rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information.

Positional Arguments

• img: UMat.
• winSize: Size.
• maxLevel: int.

Keyword Arguments

• pyramid: vector_UMat.
• withDerivatives: bool.
• pyrBorder: int.
• derivBorder: int.
• tryReuseInputImage: bool.

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Positional Arguments

• img: UMat.
• winSize: Size.
• maxLevel: int.

Keyword Arguments

• pyramid: vector_UMat.
• withDerivatives: bool.
• pyrBorder: int.
• derivBorder: int.
• tryReuseInputImage: bool.

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Positional Arguments

• images: vector_UMat
• channels: vector_int
• hist: UMat
• ranges: vector_float
• scale: double

Keyword Arguments

• dst: UMat.

Has overloading in C++

Positional Arguments

• images: vector_UMat
• channels: vector_int
• hist: UMat
• ranges: vector_float
• scale: double

Keyword Arguments

• dst: UMat.

Has overloading in C++

Positional Arguments

• samples: UMat.
• mean: UMat.
• flags: int.

Keyword Arguments

• covar: UMat.
• ctype: int.

Has overloading in C++

Positional Arguments

• samples: UMat.
• mean: UMat.
• flags: int.

Keyword Arguments

• covar: UMat.
• ctype: int.

Has overloading in C++

Positional Arguments

• images: vector_UMat
• channels: vector_int
• mask: UMat
• histSize: vector_int
• ranges: vector_float

Keyword Arguments

• hist: UMat.
• accumulate: bool.

Has overloading in C++

Positional Arguments

• images: vector_UMat
• channels: vector_int
• mask: UMat
• histSize: vector_int
• ranges: vector_float

Keyword Arguments

• hist: UMat.
• accumulate: bool.

Has overloading in C++

Computes a dense optical flow using the Gunnar Farneback's algorithm.

Positional Arguments

• prevImg: UMat.
• nextImg: UMat.
• prevPts: UMat.
• nextPts: UMat.

Keyword Arguments

• status: UMat.
• err: UMat.
• winSize: Size.
• maxLevel: int.
• criteria: TermCriteria.
• flags: int.
• minEigThreshold: double.

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Positional Arguments

• prevImg: UMat.
• nextImg: UMat.
• prevPts: UMat.
• nextPts: UMat.

Keyword Arguments

• status: UMat.
• err: UMat.
• winSize: Size.
• maxLevel: int.
• criteria: TermCriteria.
• flags: int.
• minEigThreshold: double.

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Positional Arguments

• objectPoints: vector_UMat
• imagePoints: vector_UMat
• imageSize: Size
• cameraMatrix: UMat
• distCoeffs: UMat

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• flags: int.
• criteria: TermCriteria.

Has overloading in C++

Positional Arguments

• objectPoints: vector_UMat
• imagePoints: vector_UMat
• imageSize: Size
• cameraMatrix: UMat
• distCoeffs: UMat

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• flags: int.
• criteria: TermCriteria.

Has overloading in C++

Positional Arguments

• objectPoints: vector_UMat.
• imagePoints: vector_UMat.
• imageSize: Size.
• cameraMatrix: UMat.
• distCoeffs: UMat.

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• stdDeviationsIntrinsics: UMat.
• stdDeviationsExtrinsics: UMat.
• perViewErrors: UMat.
• flags: int.
• criteria: TermCriteria.

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Positional Arguments

• objectPoints: vector_UMat.
• imagePoints: vector_UMat.
• imageSize: Size.
• cameraMatrix: UMat.
• distCoeffs: UMat.

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• stdDeviationsIntrinsics: UMat.
• stdDeviationsExtrinsics: UMat.
• perViewErrors: UMat.
• flags: int.
• criteria: TermCriteria.

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Positional Arguments

• objectPoints: vector_UMat
• imagePoints: vector_UMat
• imageSize: Size
• iFixedPoint: int
• cameraMatrix: UMat
• distCoeffs: UMat

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• newObjPoints: UMat.
• flags: int.
• criteria: TermCriteria.

Has overloading in C++

Positional Arguments

• objectPoints: vector_UMat
• imagePoints: vector_UMat
• imageSize: Size
• iFixedPoint: int
• cameraMatrix: UMat
• distCoeffs: UMat

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• newObjPoints: UMat.
• flags: int.
• criteria: TermCriteria.

Has overloading in C++

Positional Arguments

• objectPoints: vector_UMat.
• imagePoints: vector_UMat.
• imageSize: Size.
• iFixedPoint: int.
• cameraMatrix: UMat.
• distCoeffs: UMat.

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• newObjPoints: UMat.
• stdDeviationsIntrinsics: UMat.
• stdDeviationsExtrinsics: UMat.
• stdDeviationsObjPoints: UMat.
• perViewErrors: UMat.
• flags: int.
• criteria: TermCriteria.

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. This function is an extension of #calibrateCamera with the method of releasing object which was proposed in @cite strobl2011iccv. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, #calibrateCamera is a wrapper for this function.

Positional Arguments

• objectPoints: vector_UMat.
• imagePoints: vector_UMat.
• imageSize: Size.
• iFixedPoint: int.
• cameraMatrix: UMat.
• distCoeffs: UMat.

Keyword Arguments

• rvecs: vector_UMat.
• tvecs: vector_UMat.
• newObjPoints: UMat.
• stdDeviationsIntrinsics: UMat.
• stdDeviationsExtrinsics: UMat.
• stdDeviationsObjPoints: UMat.
• perViewErrors: UMat.
• flags: int.
• criteria: TermCriteria.

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. This function is an extension of #calibrateCamera with the method of releasing object which was proposed in @cite strobl2011iccv. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, #calibrateCamera is a wrapper for this function.

Positional Arguments

• r_gripper2base: vector_UMat.
• t_gripper2base: vector_UMat.
• r_target2cam: vector_UMat.
• t_target2cam: vector_UMat.

Keyword Arguments

• r_cam2gripper: UMat.
• t_cam2gripper: UMat.
• method: HandEyeCalibrationMethod.

Computes Hand-Eye calibration: \f$_{}^{g}\textrm{T}_c\f$

Positional Arguments

• r_gripper2base: vector_UMat.
• t_gripper2base: vector_UMat.
• r_target2cam: vector_UMat.
• t_target2cam: vector_UMat.

Keyword Arguments

• r_cam2gripper: UMat.
• t_cam2gripper: UMat.
• method: HandEyeCalibrationMethod.

Computes Hand-Eye calibration: \f$_{}^{g}\textrm{T}_c\f$

Positional Arguments

• r_world2cam: vector_UMat.
• t_world2cam: vector_UMat.
• r_base2gripper: vector_UMat.
• t_base2gripper: vector_UMat.

Keyword Arguments

• r_base2world: UMat.
• t_base2world: UMat.
• r_gripper2cam: UMat.
• t_gripper2cam: UMat.
• method: RobotWorldHandEyeCalibrationMethod.

Computes Robot-World/Hand-Eye calibration: \f$_{}^{w}\textrm{T}_b\f$ and \f$_{}^{c}\textrm{T}_g\f$

Positional Arguments

• r_world2cam: vector_UMat.
• t_world2cam: vector_UMat.
• r_base2gripper: vector_UMat.
• t_base2gripper: vector_UMat.

Keyword Arguments

• r_base2world: UMat.
• t_base2world: UMat.
• r_gripper2cam: UMat.
• t_gripper2cam: UMat.
• method: RobotWorldHandEyeCalibrationMethod.

Computes Robot-World/Hand-Eye calibration: \f$_{}^{w}\textrm{T}_b\f$ and \f$_{}^{c}\textrm{T}_g\f$

Computes useful camera characteristics from the camera intrinsic matrix.

Finds an object center, size, and orientation.

Positional Arguments

• image: UMat.
• threshold1: double.
• threshold2: double.

Keyword Arguments

• edges: UMat.
• apertureSize: int.
• l2gradient: bool.

Finds edges in an image using the Canny algorithm @cite Canny86 . The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See http://en.wikipedia.org/wiki/Canny_edge_detector

Variaint 1:

Positional Arguments

• dx: UMat.
• dy: UMat.
• threshold1: double.
• threshold2: double.

Keyword Arguments

• edges: UMat.

Positional Arguments

• dx: UMat.
• dy: UMat.
• threshold1: double.
• threshold2: double.

Keyword Arguments

• edges: UMat.

Positional Arguments

• x: UMat.
• y: UMat.

Keyword Arguments

• magnitude: UMat.
• angle: UMat.
• angleInDegrees: bool.

Calculates the magnitude and angle of 2D vectors. The function cv::cartToPolar calculates either the magnitude, angle, or both for every 2D vector (x(I),y(I)): \f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] The angles are calculated with accuracy about 0.3 degrees. For the point (0,0), the angle is set to 0.

Positional Arguments

• x: UMat.
• y: UMat.

Keyword Arguments

• magnitude: UMat.
• angle: UMat.
• angleInDegrees: bool.

Calculates the magnitude and angle of 2D vectors. The function cv::cartToPolar calculates either the magnitude, angle, or both for every 2D vector (x(I),y(I)): \f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] The angles are calculated with accuracy about 0.3 degrees. For the point (0,0), the angle is set to 0.

Python prototype (for reference):

Returns true if the specified feature is supported by the host hardware. The function returns true if the host hardware supports the specified feature. When user calls setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code in OpenCV.

Positional Arguments

• a: UMat.

Keyword Arguments

• quiet: bool.
• minVal: double.
• maxVal: double.

Checks every element of an input array for invalid values. The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal > -DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and maxVal. In case of multi-channel arrays, each channel is processed independently. If some values are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the function either returns false (when quiet=true) or throws an exception.

Positional Arguments

• a: UMat.

Keyword Arguments

• quiet: bool.
• minVal: double.
• maxVal: double.

Checks every element of an input array for invalid values. The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal > -DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and maxVal. In case of multi-channel arrays, each channel is processed independently. If some values are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the function either returns false (when quiet=true) or throws an exception.

Positional Arguments

• img: UMat.
• center: Point.
• radius: int.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• shift: int.

Draws a circle. The function cv::circle draws a simple or filled circle with a given center and radius.

Positional Arguments

• img: UMat.
• center: Point.
• radius: int.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• shift: int.

Draws a circle. The function cv::circle draws a simple or filled circle with a given center and radius.

Has overloading in C++

Positional Arguments

• src: UMat.
• mask: UMat.

Keyword Arguments

• dst: UMat.
• red_mul: float.
• green_mul: float.
• blue_mul: float.

Given an original color image, two differently colored versions of this image can be mixed seamlessly. Multiplication factor is between .5 to 2.5.

Positional Arguments

• src: UMat.
• mask: UMat.

Keyword Arguments

• dst: UMat.
• red_mul: float.
• green_mul: float.
• blue_mul: float.

Given an original color image, two differently colored versions of this image can be mixed seamlessly. Multiplication factor is between .5 to 2.5.

Positional Arguments

• src1: UMat.
• src2: UMat.
• cmpop: int.

Keyword Arguments

• dst: UMat.

Performs the per-element comparison of two arrays or an array and scalar value. The function compares

Positional Arguments

• src1: UMat.
• src2: UMat.
• cmpop: int.

Keyword Arguments

• dst: UMat.

Performs the per-element comparison of two arrays or an array and scalar value. The function compares

Compares two histograms. The function cv::compareHist compares two dense or two sparse histograms using the specified method. The function returns \f$d(H_1, H_2)\f$ . While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the #EMD function.

Positional Arguments

• m: UMat.

Keyword Arguments

• lowerToUpper: bool.

Copies the lower or the upper half of a square matrix to its another half. The function cv::completeSymm copies the lower or the upper half of a square matrix to its another half. The matrix diagonal remains unchanged

Positional Arguments

• m: UMat.

Keyword Arguments

• lowerToUpper: bool.

Copies the lower or the upper half of a square matrix to its another half. The function cv::completeSymm copies the lower or the upper half of a square matrix to its another half. The matrix diagonal remains unchanged

Positional Arguments

• rvec1: UMat.
• tvec1: UMat.
• rvec2: UMat.
• tvec2: UMat.

Keyword Arguments

• rvec3: UMat.
• tvec3: UMat.
• dr3dr1: UMat.
• dr3dt1: UMat.
• dr3dr2: UMat.
• dr3dt2: UMat.
• dt3dr1: UMat.
• dt3dt1: UMat.
• dt3dr2: UMat.
• dt3dt2: UMat.

Combines two rotation-and-shift transformations. The functions compute: \f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and \f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

Positional Arguments

• rvec1: UMat.
• tvec1: UMat.
• rvec2: UMat.
• tvec2: UMat.

Keyword Arguments

• rvec3: UMat.
• tvec3: UMat.
• dr3dr1: UMat.
• dr3dt1: UMat.
• dr3dr2: UMat.
• dr3dt2: UMat.
• dt3dr1: UMat.
• dt3dt1: UMat.
• dt3dr2: UMat.
• dt3dt2: UMat.

Combines two rotation-and-shift transformations. The functions compute: \f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and \f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

Positional Arguments

• points: UMat.
• whichImage: int.
• f: UMat.

Keyword Arguments

• lines: UMat.

For points in an image of a stereo pair, computes the corresponding epilines in the other image.

Positional Arguments

• points: UMat.
• whichImage: int.
• f: UMat.

Keyword Arguments

• lines: UMat.

For points in an image of a stereo pair, computes the corresponding epilines in the other image.

Positional Arguments

• templateImage: UMat.
• inputImage: UMat.

Keyword Arguments

• inputMask: UMat.

Computes the Enhanced Correlation Coefficient value between two images @cite EP08 .

Positional Arguments

• templateImage: UMat.
• inputImage: UMat.

Keyword Arguments

• inputMask: UMat.

Computes the Enhanced Correlation Coefficient value between two images @cite EP08 .

Positional Arguments

• image: UMat.

Keyword Arguments

• labels: UMat.
• connectivity: int.
• ltype: int.

Has overloading in C++

Positional Arguments

• image: UMat.

Keyword Arguments

• labels: UMat.
• connectivity: int.
• ltype: int.

Has overloading in C++

Positional Arguments

• image: UMat.
• connectivity: int.
• ltype: int.
• ccltype: int.

Keyword Arguments

• labels: UMat.

computes the connected components labeled image of boolean image image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.

Positional Arguments

• image: UMat.
• connectivity: int.
• ltype: int.
• ccltype: int.

Keyword Arguments

• labels: UMat.

computes the connected components labeled image of boolean image image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.

Positional Arguments

• image: UMat.

Keyword Arguments

• labels: UMat.
• stats: UMat.
• centroids: UMat.
• connectivity: int.
• ltype: int.

Has overloading in C++

Positional Arguments

• image: UMat.

Keyword Arguments

• labels: UMat.
• stats: UMat.
• centroids: UMat.
• connectivity: int.
• ltype: int.

Has overloading in C++

Positional Arguments

• image: UMat.
• connectivity: int.
• ltype: int.
• ccltype: int.

Keyword Arguments

• labels: UMat.
• stats: UMat.
• centroids: UMat.

computes the connected components labeled image of boolean image and also produces a statistics output for each label image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms (statistics included) if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.

Positional Arguments

• image: UMat.
• connectivity: int.
• ltype: int.
• ccltype: int.

Keyword Arguments

• labels: UMat.
• stats: UMat.
• centroids: UMat.

computes the connected components labeled image of boolean image and also produces a statistics output for each label image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms (statistics included) if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.

Positional Arguments

• contour: UMat.

Keyword Arguments

• oriented: bool.

Calculates a contour area. The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong results for contours with self-intersections. Example

Positional Arguments

• contour: UMat.

Keyword Arguments

• oriented: bool.

Calculates a contour area. The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong results for contours with self-intersections. Example

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Converts an array to half precision floating number. This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. The format of half precision floating point is defined in IEEE 754-2008.

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Converts an array to half precision floating number. This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. The format of half precision floating point is defined in IEEE 754-2008.

Positional Arguments

• map1: UMat.
• map2: UMat.
• dstmap1type: int.

Keyword Arguments

• dstmap1: UMat.
• dstmap2: UMat.
• nninterpolation: bool.

Converts image transformation maps from one representation to another. The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are supported

Positional Arguments

• map1: UMat.
• map2: UMat.
• dstmap1type: int.

Keyword Arguments

• dstmap1: UMat.
• dstmap2: UMat.
• nninterpolation: bool.

Converts image transformation maps from one representation to another. The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are supported

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Converts points from homogeneous to Euclidean space. The function converts points homogeneous to Euclidean space using perspective projection. That is, each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the output point coordinates will be (0,0,0,...).

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Converts points from homogeneous to Euclidean space. The function converts points homogeneous to Euclidean space using perspective projection. That is, each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the output point coordinates will be (0,0,0,...).

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Converts points from Euclidean to homogeneous space. The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Converts points from Euclidean to homogeneous space. The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• alpha: double.
• beta: double.

Scales, calculates absolute values, and converts the result to 8-bit. On each element of the input array, the function convertScaleAbs performs three operations sequentially: scaling, taking an absolute value, conversion to an unsigned 8-bit type: \f[\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] In case of multi-channel arrays, the function processes each channel independently. When the output is not 8-bit, the operation can be emulated by calling the Mat::convertTo method (or by using matrix expressions) and then by calculating an absolute value of the result. For example

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• alpha: double.
• beta: double.

Scales, calculates absolute values, and converts the result to 8-bit. On each element of the input array, the function convertScaleAbs performs three operations sequentially: scaling, taking an absolute value, conversion to an unsigned 8-bit type: \f[\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] In case of multi-channel arrays, the function processes each channel independently. When the output is not 8-bit, the operation can be emulated by calling the Mat::convertTo method (or by using matrix expressions) and then by calculating an absolute value of the result. For example

Positional Arguments

• points: UMat.

Keyword Arguments

• hull: UMat.
• clockwise: bool.
• returnPoints: bool.

Finds the convex hull of a point set. The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 that has O(N logN) complexity in the current implementation.

Positional Arguments

• points: UMat.

Keyword Arguments

• hull: UMat.
• clockwise: bool.
• returnPoints: bool.

Finds the convex hull of a point set. The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 that has O(N logN) complexity in the current implementation.

Positional Arguments

• contour: UMat.
• convexhull: UMat.

Keyword Arguments

• convexityDefects: UMat.

Finds the convexity defects of a contour. The figure below displays convexity defects of a hand contour:

Positional Arguments

• contour: UMat.
• convexhull: UMat.

Keyword Arguments

• convexityDefects: UMat.

Finds the convexity defects of a contour. The figure below displays convexity defects of a hand contour:

Positional Arguments

• src: UMat.
• top: int.
• bottom: int.
• left: int.
• right: int.
• borderType: int.

Keyword Arguments

• dst: UMat.
• value: Scalar.

Forms a border around an image. The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling. The function supports the mode when src is already in the middle of dst . In this case, the function does not copy src itself but simply constructs the border, for example

Positional Arguments

• src: UMat.
• top: int.
• bottom: int.
• left: int.
• right: int.
• borderType: int.

Keyword Arguments

• dst: UMat.
• value: Scalar.

Forms a border around an image. The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling. The function supports the mode when src is already in the middle of dst . In this case, the function does not copy src itself but simply constructs the border, for example

Positional Arguments

• src: UMat.
• mask: UMat.

Keyword Arguments

• dst: UMat.

This is an overloaded member function, provided for convenience (python) Copies the matrix to another one. When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data.

Positional Arguments

• src: UMat.
• mask: UMat.

Keyword Arguments

• dst: UMat.

This is an overloaded member function, provided for convenience (python) Copies the matrix to another one. When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data.

Positional Arguments

• src: UMat.
• blockSize: int.
• ksize: int.

Keyword Arguments

• dst: UMat.
• borderType: int.

Calculates eigenvalues and eigenvectors of image blocks for corner detection. For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: \f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] where the derivatives are computed using the Sobel operator. After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as \f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where

Positional Arguments

• src: UMat.
• blockSize: int.
• ksize: int.

Keyword Arguments

• dst: UMat.
• borderType: int.

Calculates eigenvalues and eigenvectors of image blocks for corner detection. For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: \f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] where the derivatives are computed using the Sobel operator. After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as \f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where

Positional Arguments

• src: UMat.
• blockSize: int.
• ksize: int.
• k: double.

Keyword Arguments

• dst: UMat.
• borderType: int.

Harris corner detector. The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it computes the following characteristic: \f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] Corners in the image can be found as the local maxima of this response map.

Positional Arguments

• src: UMat.
• blockSize: int.
• ksize: int.
• k: double.

Keyword Arguments

• dst: UMat.
• borderType: int.

Harris corner detector. The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it computes the following characteristic: \f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] Corners in the image can be found as the local maxima of this response map.

Positional Arguments

• src: UMat.
• blockSize: int.

Keyword Arguments

• dst: UMat.
• ksize: int.
• borderType: int.

Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms of the formulae in the cornerEigenValsAndVecs description.

Positional Arguments

• src: UMat.
• blockSize: int.

Keyword Arguments

• dst: UMat.
• ksize: int.
• borderType: int.

Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms of the formulae in the cornerEigenValsAndVecs description.

Refines the corner locations. The function iterates to find the sub-pixel accurate location of corners or radial saddle points as described in @cite forstner1987fast, and as shown on the figure below. Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$ to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$ subject to image and measurement noise. Consider the expression: \f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f] where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up with \f$\epsilon_i\f$ set to zero: \f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f] where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first gradient term \f$G\f$ and the second gradient term \f$b\f$ gives: \f[q = G^{-1} \cdot b\f] The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates until the center stays within a set threshold.

Positional Arguments

• f: UMat.
• points1: UMat.
• points2: UMat.

Keyword Arguments

• newPoints1: UMat.
• newPoints2: UMat.

Refines coordinates of corresponding points. The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). For each given point correspondence points1[i] \<-> points2[i], and a fundamental matrix F, it computes the corrected correspondences newPoints1[i] \<-> newPoints2[i] that minimize the geometric error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint \f$newPoints2^T * F * newPoints1 = 0\f$ .

Positional Arguments

• f: UMat.
• points1: UMat.
• points2: UMat.

Keyword Arguments

• newPoints1: UMat.
• newPoints2: UMat.

Refines coordinates of corresponding points. The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). For each given point correspondence points1[i] \<-> points2[i], and a fundamental matrix F, it computes the corrected correspondences newPoints1[i] \<-> newPoints2[i] that minimize the geometric error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint \f$newPoints2^T * F * newPoints1 = 0\f$ .

Counts non-zero array elements. The function returns the number of non-zero elements in src : \f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f] @sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix

Positional Arguments

Keyword Arguments

• max_bits: int.
• exclude_range: int.
• cut: bool.

Creates AlignMTB object

Positional Arguments

Keyword Arguments

• max_bits: int.
• exclude_range: int.
• cut: bool.

Creates AlignMTB object

Positional Arguments

Keyword Arguments

• history: int.
• dist2Threshold: double.
• detectShadows: bool.

Creates KNN Background Subtractor

Positional Arguments

Keyword Arguments

• history: int.
• dist2Threshold: double.
• detectShadows: bool.

Creates KNN Background Subtractor

Positional Arguments

Keyword Arguments

• history: int.
• varThreshold: double.
• detectShadows: bool.

Creates MOG2 Background Subtractor

Positional Arguments

Keyword Arguments

• history: int.
• varThreshold: double.
• detectShadows: bool.

Creates MOG2 Background Subtractor

Positional Arguments

Keyword Arguments

• samples: int.
• lambda: float.
• random: bool.

Creates CalibrateDebevec object

Positional Arguments

Keyword Arguments

• samples: int.
• lambda: float.
• random: bool.

Creates CalibrateDebevec object

Positional Arguments

Keyword Arguments

• max_iter: int.
• threshold: float.

Creates CalibrateRobertson object

Positional Arguments

Keyword Arguments

• max_iter: int.
• threshold: float.

Creates CalibrateRobertson object

Positional Arguments

Keyword Arguments

• clipLimit: double.
• tileGridSize: Size.

Creates a smart pointer to a cv::CLAHE class and initializes it.

Positional Arguments

Keyword Arguments

• clipLimit: double.
• tileGridSize: Size.

Creates a smart pointer to a cv::CLAHE class and initializes it.

Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.

Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.

Positional Arguments

• winSize: Size.
• type: int.

Keyword Arguments

• dst: UMat.

This function computes a Hanning window coefficients in two dimensions. See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) for more information. An example is shown below

Positional Arguments

• winSize: Size.
• type: int.

Keyword Arguments

• dst: UMat.

This function computes a Hanning window coefficients in two dimensions. See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) for more information. An example is shown below

Positional Arguments

Keyword Arguments

• refine: int.
• scale: double.
• sigma_scale: double.
• quant: double.
• ang_th: double.
• log_eps: double.
• density_th: double.
• n_bins: int.

Creates a smart pointer to a LineSegmentDetector object and initializes it. The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.

Positional Arguments

Keyword Arguments

• refine: int.
• scale: double.
• sigma_scale: double.
• quant: double.
• ang_th: double.
• log_eps: double.
• density_th: double.
• n_bins: int.

Creates a smart pointer to a LineSegmentDetector object and initializes it. The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.

Creates MergeDebevec object

Positional Arguments

Keyword Arguments

• contrast_weight: float.
• saturation_weight: float.
• exposure_weight: float.

Creates MergeMertens object

Positional Arguments

Keyword Arguments

• contrast_weight: float.
• saturation_weight: float.
• exposure_weight: float.

Creates MergeMertens object

Creates MergeRobertson object

Positional Arguments

Keyword Arguments

• gamma: float.

Creates simple linear mapper with gamma correction

Positional Arguments

Keyword Arguments

• gamma: float.

Creates simple linear mapper with gamma correction

Positional Arguments

Keyword Arguments

• gamma: float.
• saturation: float.
• bias: float.

Creates TonemapDrago object

Positional Arguments

Keyword Arguments

• gamma: float.
• saturation: float.
• bias: float.

Creates TonemapDrago object

Positional Arguments

Keyword Arguments

• gamma: float.
• scale: float.
• saturation: float.

Creates TonemapMantiuk object

Positional Arguments

Keyword Arguments

• gamma: float.
• scale: float.
• saturation: float.

Creates TonemapMantiuk object

Positional Arguments

Keyword Arguments

• gamma: float.
• intensity: float.
• light_adapt: float.
• color_adapt: float.

Creates TonemapReinhard object

Positional Arguments

Keyword Arguments

• gamma: float.
• intensity: float.
• light_adapt: float.
• color_adapt: float.

Creates TonemapReinhard object

Computes the cube root of an argument. The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly. NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for single-precision data.

Positional Arguments

• src: UMat.
• code: int.

Keyword Arguments

• dst: UMat.
• dstCn: int.

Converts an image from one color space to another. The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. The conventional ranges for R, G, and B channel values are

Positional Arguments

• src: UMat.
• code: int.

Keyword Arguments

• dst: UMat.
• dstCn: int.

Converts an image from one color space to another. The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. The conventional ranges for R, G, and B channel values are

Positional Arguments

• src1: UMat.
• src2: UMat.
• code: int.

Keyword Arguments

• dst: UMat.

Converts an image from one color space to another where the source image is stored in two planes. This function only supports YUV420 to RGB conversion as of now.

Positional Arguments

• src1: UMat.
• src2: UMat.
• code: int.

Keyword Arguments

• dst: UMat.

Converts an image from one color space to another where the source image is stored in two planes. This function only supports YUV420 to RGB conversion as of now.

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.

Performs a forward or inverse discrete Cosine transform of 1D or 2D array. The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D floating-point array

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.

Performs a forward or inverse discrete Cosine transform of 1D or 2D array. The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D floating-point array

Positional Arguments

• src: UMat.

Keyword Arguments

• grayscale: UMat.
• color_boost: UMat.

Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized black-and-white photograph rendering, and in many single channel image processing applications @cite CL12 . This function is to be applied on color images.

Positional Arguments

• src: UMat.

Keyword Arguments

• grayscale: UMat.
• color_boost: UMat.

Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized black-and-white photograph rendering, and in many single channel image processing applications @cite CL12 . This function is to be applied on color images.

Positional Arguments

• e: UMat.

Keyword Arguments

• r1: UMat.
• r2: UMat.
• t: UMat.

Decompose an essential matrix to possible rotations and translation. This function decomposes the essential matrix E using svd decomposition @cite HartleyZ00. In general, four possible poses exist for the decomposition of E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. If E gives the epipolar constraint \f$[p_2; 1]^T A^{-T} E A^{-1} [p_1; 1] = 0\f$ between the image points \f$p_1\f$ in the first image and \f$p_2\f$ in second image, then any of the tuples \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$ is a change of basis from the first camera's coordinate system to the second camera's coordinate system. However, by decomposing E, one can only get the direction of the translation. For this reason, the translation t is returned with unit length.

Positional Arguments

• e: UMat.

Keyword Arguments

• r1: UMat.
• r2: UMat.
• t: UMat.

Decompose an essential matrix to possible rotations and translation. This function decomposes the essential matrix E using svd decomposition @cite HartleyZ00. In general, four possible poses exist for the decomposition of E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. If E gives the epipolar constraint \f$[p_2; 1]^T A^{-T} E A^{-1} [p_1; 1] = 0\f$ between the image points \f$p_1\f$ in the first image and \f$p_2\f$ in second image, then any of the tuples \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$ is a change of basis from the first camera's coordinate system to the second camera's coordinate system. However, by decomposing E, one can only get the direction of the translation. For this reason, the translation t is returned with unit length.

Positional Arguments

• h: UMat.
• k: UMat.

Keyword Arguments

• rotations: vector_UMat.
• translations: vector_UMat.
• normals: vector_UMat.

Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). This function extracts relative camera motion between two views of a planar object and returns up to four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of the homography matrix H is described in detail in @cite Malis. If the homography H, induced by the plane, gives the constraint \f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] on the source image points \f$p_i\f$ and the destination image points \f$p'_i\f$, then the tuple of rotations[k] and translations[k] is a change of basis from the source camera's coordinate system to the destination camera's coordinate system. However, by decomposing H, one can only get the translation normalized by the (typically unknown) depth of the scene, i.e. its direction but with normalized length. If point correspondences are available, at least two solutions may further be invalidated, by applying positive depth constraint, i.e. all points must be in front of the camera.

Positional Arguments

• h: UMat.
• k: UMat.

Keyword Arguments

• rotations: vector_UMat.
• translations: vector_UMat.
• normals: vector_UMat.

Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). This function extracts relative camera motion between two views of a planar object and returns up to four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of the homography matrix H is described in detail in @cite Malis. If the homography H, induced by the plane, gives the constraint \f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] on the source image points \f$p_i\f$ and the destination image points \f$p'_i\f$, then the tuple of rotations[k] and translations[k] is a change of basis from the source camera's coordinate system to the destination camera's coordinate system. However, by decomposing H, one can only get the translation normalized by the (typically unknown) depth of the scene, i.e. its direction but with normalized length. If point correspondences are available, at least two solutions may further be invalidated, by applying positive depth constraint, i.e. all points must be in front of the camera.

Positional Arguments

• projMatrix: UMat.

Keyword Arguments

• cameraMatrix: UMat.
• rotMatrix: UMat.
• transVect: UMat.
• rotMatrixX: UMat.
• rotMatrixY: UMat.
• rotMatrixZ: UMat.
• eulerAngles: UMat.

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

Positional Arguments

• projMatrix: UMat.

Keyword Arguments

• cameraMatrix: UMat.
• rotMatrix: UMat.
• transVect: UMat.
• rotMatrixX: UMat.
• rotMatrixY: UMat.
• rotMatrixZ: UMat.
• eulerAngles: UMat.

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

Positional Arguments

• src: UMat.
• code: int.

Keyword Arguments

• dst: UMat.
• dstCn: int.

main function for all demosaicing processes

Positional Arguments

• src: UMat.
• code: int.

Keyword Arguments

• dst: UMat.
• dstCn: int.

main function for all demosaicing processes

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• sigma_s: float.
• sigma_r: float.

This filter enhances the details of a particular image.

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• sigma_s: float.
• sigma_r: float.

This filter enhances the details of a particular image.

Returns the determinant of a square floating-point matrix. The function cv::determinant calculates and returns the determinant of the specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the direct method is used. For larger matrices, the function uses LU factorization with partial pivoting. For symmetric positively-determined matrices, it is also possible to use eigen decomposition to calculate the determinant.

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.
• nonzeroRows: int.

Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. The function cv::dft performs one of the following

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.
• nonzeroRows: int.

Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. The function cv::dft performs one of the following

Positional Arguments

• src: UMat.
• kernel: UMat.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• iterations: int.
• borderType: int.
• borderValue: Scalar.

Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

Positional Arguments

• src: UMat.
• kernel: UMat.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• iterations: int.
• borderType: int.
• borderValue: Scalar.

Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

Positional Arguments

• winname: String.
• text: String.

Keyword Arguments

• delayms: int.

Displays a text on a window image as an overlay for a specified duration. The function displayOverlay displays useful information/tips on top of the window for a certain amount of time delayms. The function does not modify the image, displayed in the window, that is, after the specified delay the original content of the window is restored.

Positional Arguments

• winname: String.
• text: String.

Keyword Arguments

• delayms: int.

Displays a text on a window image as an overlay for a specified duration. The function displayOverlay displays useful information/tips on top of the window for a certain amount of time delayms. The function does not modify the image, displayed in the window, that is, after the specified delay the original content of the window is restored.

Positional Arguments

• winname: String.
• text: String.

Keyword Arguments

• delayms: int.

Displays a text on the window statusbar during the specified period of time. The function displayStatusBar displays useful information/tips on top of the window for a certain amount of time delayms . This information is displayed on the window statusbar (the window must be created with the CV_GUI_EXPANDED flags).

Positional Arguments

• winname: String.
• text: String.

Keyword Arguments

• delayms: int.

Displays a text on the window statusbar during the specified period of time. The function displayStatusBar displays useful information/tips on top of the window for a certain amount of time delayms . This information is displayed on the window statusbar (the window must be created with the CV_GUI_EXPANDED flags).

Positional Arguments

• src: UMat.
• distanceType: int.
• maskSize: int.

Keyword Arguments

• dst: UMat.
• dstType: int.

Has overloading in C++

Positional Arguments

• src: UMat.
• distanceType: int.
• maskSize: int.

Keyword Arguments

• dst: UMat.
• dstType: int.

Has overloading in C++

Positional Arguments

• src: UMat.
• distanceType: int.
• maskSize: int.

Keyword Arguments

• dst: UMat.
• labels: UMat.
• labelType: int.

Calculates the distance to the closest zero pixel for each pixel of the source image. The function cv::distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted as b), and all knight's moves must have the same cost (denoted as c). For the #DIST_C and #DIST_L1 types, the distance is calculated precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a \f$5\times 5\f$ mask gives more accurate results). For a,b, and c, OpenCV uses the values suggested in the original paper

Positional Arguments

• src: UMat.
• distanceType: int.
• maskSize: int.

Keyword Arguments

• dst: UMat.
• labels: UMat.
• labelType: int.

Calculates the distance to the closest zero pixel for each pixel of the source image. The function cv::distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted as b), and all knight's moves must have the same cost (denoted as c). For the #DIST_C and #DIST_L1 types, the distance is calculated precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a \f$5\times 5\f$ mask gives more accurate results). For a,b, and c, OpenCV uses the values suggested in the original paper

Variaint 1:

Positional Arguments

• scale: double
• src2: UMat

Keyword Arguments

• dst: UMat.
• dtype: int.

Has overloading in C++

Variaint 1:

Positional Arguments

• scale: double
• src2: UMat

Keyword Arguments

• dst: UMat.
• dtype: int.

Has overloading in C++

Positional Arguments

• a: UMat.
• b: UMat.
• flags: int.

Keyword Arguments

• c: UMat.
• conjB: bool.

Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.

Positional Arguments

• a: UMat.
• b: UMat.
• flags: int.

Keyword Arguments

• c: UMat.
• conjB: bool.

Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.

Renders the detected chessboard corners.

Positional Arguments

• image: UMat.
• contours: vector_UMat.
• contourIdx: int.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• hierarchy: UMat.
• maxLevel: int.
• offset: Point.

Draws contours outlines or filled contours. The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve connected components from the binary image and label them: : @include snippets/imgproc_drawContours.cpp

Positional Arguments

• image: UMat.
• contours: vector_UMat.
• contourIdx: int.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• hierarchy: UMat.
• maxLevel: int.
• offset: Point.

Draws contours outlines or filled contours. The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve connected components from the binary image and label them: : @include snippets/imgproc_drawContours.cpp

Positional Arguments

• image: UMat.
• cameraMatrix: UMat.
• distCoeffs: UMat.
• rvec: UMat.
• tvec: UMat.
• length: float.

Keyword Arguments

• thickness: int.

Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP

Positional Arguments

• image: UMat.
• cameraMatrix: UMat.
• distCoeffs: UMat.
• rvec: UMat.
• tvec: UMat.
• length: float.

Keyword Arguments

• thickness: int.

Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP

Positional Arguments

• image: UMat.
• keypoints: vector_KeyPoint.
• outImage: UMat.

Keyword Arguments

• color: Scalar.
• flags: DrawMatchesFlags.

Draws keypoints.

Positional Arguments

• image: UMat.
• keypoints: vector_KeyPoint.
• outImage: UMat.

Keyword Arguments

• color: Scalar.
• flags: DrawMatchesFlags.

Draws keypoints.

Positional Arguments

• img: UMat. Image.
• position: Point.
• color: Scalar.

Keyword Arguments

• markerType: int.
• markerSize: int.
• thickness: int.
• line_type: int.

Draws a marker on a predefined position in an image. The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.

Positional Arguments

• img: UMat. Image.
• position: Point.
• color: Scalar.

Keyword Arguments

• markerType: int.
• markerSize: int.
• thickness: int.
• line_type: int.

Draws a marker on a predefined position in an image. The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.

Positional Arguments

• img1: UMat.
• keypoints1: vector_KeyPoint.
• img2: UMat.
• keypoints2: vector_KeyPoint.
• matches1to2: vector_DMatch.
• outImg: UMat.

Keyword Arguments

• matchColor: Scalar.
• singlePointColor: Scalar.
• matchesMask: vector_char.
• flags: DrawMatchesFlags.

Draws the found matches of keypoints from two images.

Variaint 1:

Positional Arguments

• img1: UMat
• keypoints1: vector_KeyPoint
• img2: UMat
• keypoints2: vector_KeyPoint
• matches1to2: vector_DMatch
• outImg: UMat
• matchesThickness: int

Keyword Arguments

• matchColor: Scalar.
• singlePointColor: Scalar.
• matchesMask: vector_char.
• flags: DrawMatchesFlags.

Has overloading in C++

Positional Arguments

• img1: UMat
• keypoints1: vector_KeyPoint
• img2: UMat
• keypoints2: vector_KeyPoint
• matches1to2: vector_DMatch
• outImg: UMat
• matchesThickness: int

Keyword Arguments

• matchColor: Scalar.
• singlePointColor: Scalar.
• matchesMask: vector_char.
• flags: DrawMatchesFlags.

Has overloading in C++

Positional Arguments

• img1: UMat
• keypoints1: vector_KeyPoint
• img2: UMat
• keypoints2: vector_KeyPoint
• matches1to2: vector_vector_DMatch
• outImg: UMat

Keyword Arguments

• matchColor: Scalar.
• singlePointColor: Scalar.
• matchesMask: vector_vector_char.
• flags: DrawMatchesFlags.

Python prototype (for reference):

Positional Arguments

• img1: UMat
• keypoints1: vector_KeyPoint
• img2: UMat
• keypoints2: vector_KeyPoint
• matches1to2: vector_vector_DMatch
• outImg: UMat

Keyword Arguments

• matchColor: Scalar.
• singlePointColor: Scalar.
• matchesMask: vector_vector_char.
• flags: DrawMatchesFlags.

Python prototype (for reference):

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.
• sigma_s: float.
• sigma_r: float.

Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications @cite EM11 .

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.
• sigma_s: float.
• sigma_r: float.

Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications @cite EM11 .

Positional Arguments

• src: UMat.

Keyword Arguments

• eigenvalues: UMat.
• eigenvectors: UMat.

Calculates eigenvalues and eigenvectors of a symmetric matrix. The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric matrix src

Positional Arguments

• src: UMat.

Keyword Arguments

• eigenvalues: UMat.
• eigenvectors: UMat.

Calculates eigenvalues and eigenvectors of a symmetric matrix. The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric matrix src

Positional Arguments

• src: UMat.

Keyword Arguments

• eigenvalues: UMat.
• eigenvectors: UMat.

Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). Note: Assumes real eigenvalues. The function calculates eigenvalues and eigenvectors (optional) of the square matrix src

Positional Arguments

• src: UMat.

Keyword Arguments

• eigenvalues: UMat.
• eigenvectors: UMat.

Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). Note: Assumes real eigenvalues. The function calculates eigenvalues and eigenvectors (optional) of the square matrix src

Approximates an elliptic arc with a polyline. The function ellipse2Poly computes the vertices of a polyline that approximates the specified elliptic arc. It is used by #ellipse. If arcStart is greater than arcEnd, they are swapped.

Positional Arguments

• img: UMat. Image.
• box: RotatedRect.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.

Has overloading in C++

Positional Arguments

• img: UMat. Image.
• box: RotatedRect.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.

Has overloading in C++

Positional Arguments

• img: UMat. Image.
• center: Point.
• axes: Size.
• angle: double.
• startAngle: double.
• endAngle: double.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• shift: int.

Draws a simple or thick elliptic arc or fills an ellipse sector. The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Positional Arguments

• img: UMat. Image.
• center: Point.
• axes: Size.
• angle: double.
• startAngle: double.
• endAngle: double.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• shift: int.

Draws a simple or thick elliptic arc or fills an ellipse sector. The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Positional Arguments

• signature1: UMat.
• signature2: UMat.
• distType: int.

Keyword Arguments

• cost: UMat.
• lowerBound: Ptr<float>.
• flow: UMat.

Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in @cite RubnerSept98, @cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.

Positional Arguments

• signature1: UMat.
• signature2: UMat.
• distType: int.

Keyword Arguments

• cost: UMat.
• lowerBound: Ptr<float>.
• flow: UMat.

Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in @cite RubnerSept98, @cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Equalizes the histogram of a grayscale image. The function equalizes the histogram of the input image using the following algorithm

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Equalizes the histogram of a grayscale image. The function equalizes the histogram of the input image using the following algorithm

Positional Arguments

• src: UMat.
• kernel: UMat.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• iterations: int.
• borderType: int.
• borderValue: Scalar.

Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

Positional Arguments

• src: UMat.
• kernel: UMat.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• iterations: int.
• borderType: int.
• borderValue: Scalar.

Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

Positional Arguments

• from: UMat.
• to: UMat.

Keyword Arguments

• inliers: UMat.
• method: int.
• ransacReprojThreshold: double.
• maxIters: size_t.
• confidence: double.
• refineIters: size_t.

Computes an optimal affine transformation between two 2D point sets. It computes \f[ \begin{bmatrix} x\\ y\\ \end{bmatrix} \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \f]

Variaint 1:

Positional Arguments

• pts1: UMat
• pts2: UMat
• params: UsacParams

Keyword Arguments

• inliers: UMat.

Python prototype (for reference):

Positional Arguments

• pts1: UMat
• pts2: UMat
• params: UsacParams

Keyword Arguments

• inliers: UMat.

Python prototype (for reference):

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• force_rotation: bool.

Computes an optimal affine transformation between two 3D point sets. It computes \f$R,s,t\f$ minimizing \f$\sum{i} dst_i - c \cdot R \cdot src_i \f$ where \f$R\f$ is a 3x3 rotation matrix, \f$t\f$ is a 3x1 translation vector and \f$s\f$ is a scalar size value. This is an implementation of the algorithm by Umeyama \cite umeyama1991least . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• force_rotation: bool.

Computes an optimal affine transformation between two 3D point sets. It computes \f$R,s,t\f$ minimizing \f$\sum{i} dst_i - c \cdot R \cdot src_i \f$ where \f$R\f$ is a 3x3 rotation matrix, \f$t\f$ is a 3x1 translation vector and \f$s\f$ is a scalar size value. This is an implementation of the algorithm by Umeyama \cite umeyama1991least . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.

Positional Arguments

• from: UMat.
• to: UMat.

Keyword Arguments

• inliers: UMat.
• method: int.
• ransacReprojThreshold: double.
• maxIters: size_t.
• confidence: double.
• refineIters: size_t.

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Positional Arguments

• from: UMat.
• to: UMat.

Keyword Arguments

• inliers: UMat.
• method: int.
• ransacReprojThreshold: double.
• maxIters: size_t.
• confidence: double.
• refineIters: size_t.

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Positional Arguments

• image: UMat.
• patternSize: Size.
• corners: UMat.

Keyword Arguments

• rise_distance: float.
• vertical: bool.
• sharpness: UMat.

Estimates the sharpness of a detected chessboard. Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels. The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness) @return Scalar(average sharpness, average min brightness, average max brightness,0)

Positional Arguments

• image: UMat.
• patternSize: Size.
• corners: UMat.

Keyword Arguments

• rise_distance: float.
• vertical: bool.
• sharpness: UMat.

Estimates the sharpness of a detected chessboard. Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels. The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness) @return Scalar(average sharpness, average min brightness, average max brightness,0)

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• out: UMat.
• inliers: UMat.
• ransacThreshold: double.
• confidence: double.

Computes an optimal translation between two 3D point sets. It computes \f[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \f]

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• out: UMat.
• inliers: UMat.
• ransacThreshold: double.
• confidence: double.

Computes an optimal translation between two 3D point sets. It computes \f[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \f]

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Calculates the exponent of every array element. The function cv::exp calculates the exponent of every element of the input array: \f[\texttt{dst} [I] = e^{ src(I) }\f] The maximum relative error is about 7e-6 for single-precision input and less than 1e-10 for double-precision input. Currently, the function converts denormalized values to zeros on output. Special values (NaN, Inf) are not handled. @sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Calculates the exponent of every array element. The function cv::exp calculates the exponent of every element of the input array: \f[\texttt{dst} [I] = e^{ src(I) }\f] The maximum relative error is about 7e-6 for single-precision input and less than 1e-10 for double-precision input. Currently, the function converts denormalized values to zeros on output. Special values (NaN, Inf) are not handled. @sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude

Positional Arguments

• src: UMat.
• coi: int.

Keyword Arguments

• dst: UMat.

Extracts a single channel from src (coi is 0-based index) @sa mixChannels, split

Positional Arguments

• src: UMat.
• coi: int.

Keyword Arguments

• dst: UMat.

Extracts a single channel from src (coi is 0-based index) @sa mixChannels, split

Calculates the angle of a 2D vector in degrees. The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• h: float.
• templateWindowSize: int.
• searchWindowSize: int.

Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise

Variaint 1:

Positional Arguments

• src: UMat.
• h: vector_float.

Keyword Arguments

• dst: UMat.
• templateWindowSize: int.
• searchWindowSize: int.
• normType: int.

Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise

Positional Arguments

• src: UMat.
• h: vector_float.

Keyword Arguments

• dst: UMat.
• templateWindowSize: int.
• searchWindowSize: int.
• normType: int.

Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• h: float.
• hColor: float.
• templateWindowSize: int.
• searchWindowSize: int.

Modification of fastNlMeansDenoising function for colored images

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• h: float.
• hColor: float.
• templateWindowSize: int.
• searchWindowSize: int.

Modification of fastNlMeansDenoising function for colored images

Positional Arguments

• srcImgs: vector_UMat.
• imgToDenoiseIndex: int.
• temporalWindowSize: int.

Keyword Arguments

• dst: UMat.
• h: float.
• hColor: float.
• templateWindowSize: int.
• searchWindowSize: int.

Modification of fastNlMeansDenoisingMulti function for colored images sequences

Positional Arguments

• srcImgs: vector_UMat.
• imgToDenoiseIndex: int.
• temporalWindowSize: int.

Keyword Arguments

• dst: UMat.
• h: float.
• hColor: float.
• templateWindowSize: int.
• searchWindowSize: int.

Modification of fastNlMeansDenoisingMulti function for colored images sequences

Positional Arguments

• srcImgs: vector_UMat.
• imgToDenoiseIndex: int.
• temporalWindowSize: int.

Keyword Arguments

• dst: UMat.
• h: float.
• templateWindowSize: int.
• searchWindowSize: int.

Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394

Variaint 1:

Positional Arguments

• srcImgs: vector_UMat.
• imgToDenoiseIndex: int.
• temporalWindowSize: int.
• h: vector_float.

Keyword Arguments

• dst: UMat.
• templateWindowSize: int.
• searchWindowSize: int.
• normType: int.

Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394

Positional Arguments

• srcImgs: vector_UMat.
• imgToDenoiseIndex: int.
• temporalWindowSize: int.
• h: vector_float.

Keyword Arguments

• dst: UMat.
• templateWindowSize: int.
• searchWindowSize: int.
• normType: int.

Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394

Positional Arguments

• img: UMat. Image.
• points: UMat.
• color: Scalar.

Keyword Arguments

• lineType: int.
• shift: int.

Fills a convex polygon. The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function #fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).

Positional Arguments

• img: UMat. Image.
• points: UMat.
• color: Scalar.

Keyword Arguments

• lineType: int.
• shift: int.

Fills a convex polygon. The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function #fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).

Positional Arguments

• img: UMat. Image.
• pts: vector_UMat.
• color: Scalar.

Keyword Arguments

• lineType: int.
• shift: int.
• offset: Point.

Fills the area bounded by one or more polygons. The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.

Positional Arguments

• img: UMat. Image.
• pts: vector_UMat.
• color: Scalar.

Keyword Arguments

• lineType: int.
• shift: int.
• offset: Point.

Fills the area bounded by one or more polygons. The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.

Positional Arguments

• src: UMat.
• ddepth: int.
• kernel: UMat.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• delta: double.
• borderType: int.

Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode. The function does actually compute correlation, not the convolution: \f[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1). The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.

Positional Arguments

• src: UMat.
• ddepth: int.
• kernel: UMat.

Keyword Arguments

• dst: UMat.
• anchor: Point.
• delta: double.
• borderType: int.

Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode. The function does actually compute correlation, not the convolution: \f[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1). The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.

Positional Arguments

• rotations: vector_UMat.
• normals: vector_UMat.
• beforePoints: UMat.
• afterPoints: UMat.

Keyword Arguments

• possibleSolutions: UMat.
• pointsMask: UMat.

Filters homography decompositions based on additional information. This function is intended to filter the output of the #decomposeHomographyMat based on additional information as described in @cite Malis . The summary of the method: the #decomposeHomographyMat function returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. The inputs are left unchanged; the filtered solution set is returned as indices into the existing one.

Positional Arguments

• rotations: vector_UMat.
• normals: vector_UMat.
• beforePoints: UMat.
• afterPoints: UMat.

Keyword Arguments

• possibleSolutions: UMat.
• pointsMask: UMat.

Filters homography decompositions based on additional information. This function is intended to filter the output of the #decomposeHomographyMat based on additional information as described in @cite Malis . The summary of the method: the #decomposeHomographyMat function returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. The inputs are left unchanged; the filtered solution set is returned as indices into the existing one.

Positional Arguments

• img: UMat.
• newVal: double.
• maxSpeckleSize: int.
• maxDiff: double.

Keyword Arguments

• buf: UMat.

Filters off small noise blobs (speckles) in the disparity map

Positional Arguments

• img: UMat.
• newVal: double.
• maxSpeckleSize: int.
• maxDiff: double.

Keyword Arguments

• buf: UMat.

Filters off small noise blobs (speckles) in the disparity map

Python prototype (for reference):

Positional Arguments

• image: UMat.
• patternSize: Size.

Keyword Arguments

• corners: UMat.
• flags: int.

Finds the positions of internal corners of the chessboard.

Positional Arguments

• image: UMat.
• patternSize: Size.

Keyword Arguments

• corners: UMat.
• flags: int.

Finds the positions of internal corners of the chessboard.

Positional Arguments

• image: UMat
• patternSize: Size

Keyword Arguments

• corners: UMat.
• flags: int.

Has overloading in C++

Positional Arguments

• image: UMat
• patternSize: Size

Keyword Arguments

• corners: UMat.
• flags: int.

Has overloading in C++

Positional Arguments

• image: UMat.
• patternSize: Size.
• flags: int.

Keyword Arguments

• corners: UMat.
• meta: UMat.

Finds the positions of internal corners of the chessboard using a sector based approach.

Positional Arguments

• image: UMat.
• patternSize: Size.
• flags: int.

Keyword Arguments

• corners: UMat.
• meta: UMat.

Finds the positions of internal corners of the chessboard using a sector based approach.

Positional Arguments

• image: UMat
• patternSize: Size

Keyword Arguments

• centers: UMat.
• flags: int.
• blobDetector: Ptr<FeatureDetector>.

Has overloading in C++

Positional Arguments

• image: UMat
• patternSize: Size

Keyword Arguments

• centers: UMat.
• flags: int.
• blobDetector: Ptr<FeatureDetector>.

Has overloading in C++

Positional Arguments

• image: UMat.
• patternSize: Size.
• flags: int.
• blobDetector: Ptr<FeatureDetector>.
• parameters: CirclesGridFinderParameters.

Keyword Arguments

• centers: UMat.

Finds centers in the grid of circles.

Positional Arguments

• image: UMat.
• patternSize: Size.
• flags: int.
• blobDetector: Ptr<FeatureDetector>.
• parameters: CirclesGridFinderParameters.

Keyword Arguments

• centers: UMat.

Finds centers in the grid of circles.

Positional Arguments

• image: UMat.
• mode: int.
• method: int.

Keyword Arguments

• contours: vector_UMat.
• hierarchy: UMat.
• offset: Point.

Finds contours in a binary image. The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the OpenCV sample directory. Note: Since opencv 3.2 source image is not modified by this function.

Positional Arguments

• image: UMat.
• mode: int.
• method: int.

Keyword Arguments

• contours: vector_UMat.
• hierarchy: UMat.
• offset: Point.

Finds contours in a binary image. The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the OpenCV sample directory. Note: Since opencv 3.2 source image is not modified by this function.

Positional Arguments

• points1: UMat.
• points2: UMat.

Keyword Arguments

• focal: double.
• pp: Point2d.
• method: int.
• prob: double.
• threshold: double.
• maxIters: int.
• mask: UMat.

Has overloading in C++

Variaint 1:

Positional Arguments

• points1: UMat.
• points2: UMat.
• cameraMatrix: UMat.

Keyword Arguments

• method: int.
• prob: double.
• threshold: double.
• maxIters: int.
• mask: UMat.

Calculates an essential matrix from the corresponding points in two images.

Positional Arguments

• points1: UMat.
• points2: UMat.
• cameraMatrix: UMat.

Keyword Arguments

• method: int.
• prob: double.
• threshold: double.
• maxIters: int.
• mask: UMat.

Calculates an essential matrix from the corresponding points in two images.

Positional Arguments

• points1: UMat.
• points2: UMat.
• cameraMatrix1: UMat.
• distCoeffs1: UMat.
• cameraMatrix2: UMat.
• distCoeffs2: UMat.

Keyword Arguments

• method: int.
• prob: double.
• threshold: double.
• mask: UMat.

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

Variaint 1:

Positional Arguments

• points1: UMat
• points2: UMat
• cameraMatrix1: UMat
• cameraMatrix2: UMat
• dist_coeff1: UMat
• dist_coeff2: UMat
• params: UsacParams

Keyword Arguments

• mask: UMat.

Python prototype (for reference):

Positional Arguments

• points1: UMat
• points2: UMat
• cameraMatrix1: UMat
• cameraMatrix2: UMat
• dist_coeff1: UMat
• dist_coeff2: UMat
• params: UsacParams

Keyword Arguments

• mask: UMat.

Python prototype (for reference):

Positional Arguments

• points1: UMat
• points2: UMat

Keyword Arguments

• method: int.
• ransacReprojThreshold: double.
• confidence: double.
• mask: UMat.

Has overloading in C++

Variaint 1:

Positional Arguments

• points1: UMat
• points2: UMat
• params: UsacParams

Keyword Arguments

• mask: UMat.

Has overloading in C++

Positional Arguments

• points1: UMat
• points2: UMat
• params: UsacParams

Keyword Arguments

• mask: UMat.

Has overloading in C++

Positional Arguments

• points1: UMat.
• points2: UMat.
• method: int.
• ransacReprojThreshold: double.
• confidence: double.
• maxIters: int.

Keyword Arguments

• mask: UMat.

Calculates a fundamental matrix from the corresponding points in two images.

Positional Arguments

• points1: UMat.
• points2: UMat.
• method: int.
• ransacReprojThreshold: double.
• confidence: double.
• maxIters: int.

Keyword Arguments

• mask: UMat.

Calculates a fundamental matrix from the corresponding points in two images.

Positional Arguments

• srcPoints: UMat.
• dstPoints: UMat.

Keyword Arguments

• method: int.
• ransacReprojThreshold: double.
• mask: UMat.
• maxIters: int.
• confidence: double.

Finds a perspective transformation between two planes.

Variaint 1:

Positional Arguments

• srcPoints: UMat
• dstPoints: UMat
• params: UsacParams

Keyword Arguments

• mask: UMat.

Has overloading in C++

Positional Arguments

• srcPoints: UMat
• dstPoints: UMat
• params: UsacParams

Keyword Arguments

• mask: UMat.

Has overloading in C++

Positional Arguments

• src: UMat.

Keyword Arguments

• idx: UMat.

Returns the list of locations of non-zero pixels Given a binary matrix (likely returned from an operation such as threshold(), compare(), >, ==, etc, return all of the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y) For example

Positional Arguments

• src: UMat.

Keyword Arguments

• idx: UMat.

Returns the list of locations of non-zero pixels Given a binary matrix (likely returned from an operation such as threshold(), compare(), >, ==, etc, return all of the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y) For example

Positional Arguments

• templateImage: UMat
• inputImage: UMat
• warpMatrix: UMat

Keyword Arguments

• motionType: int.
• criteria: TermCriteria.
• inputMask: UMat.

Has overloading in C++

Positional Arguments

• templateImage: UMat
• inputImage: UMat
• warpMatrix: UMat

Keyword Arguments

• motionType: int.
• criteria: TermCriteria.
• inputMask: UMat.

Has overloading in C++

Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .

Fits an ellipse around a set of 2D points. The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.

Fits an ellipse around a set of 2D points. The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used. For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves by imposing the condition that \f$ A^T ( D_x^T D_x + D_y^T D_y) A = 1 \f$ where the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with respect to x and y. The matrices are formed row by row applying the following to each of the points in the set: \f{align}{ D(i,:)&=\left{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right} & D_x(i,:)&=\left{2 x_i,y_i,0,1,0,0\right} & D_y(i,:)&=\left{0,x_i,2 y_i,0,1,0\right} \f} The AMS method minimizes the cost function \f{equation}{ \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \f} The minimum cost is found by solving the generalized eigenvalue problem. \f{equation*}{ D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \f}

Fits an ellipse around a set of 2D points. The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Direct least square (Direct) method by @cite Fitzgibbon1999 is used. For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$. The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality and as the coefficients can be arbitrarily scaled is not overly restrictive. \f{equation}{ \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} 0 & 0 & 2 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 & 0 & 0 \\ 2 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \end{matrix} \right) \f} The minimum cost is found by solving the generalized eigenvalue problem. \f{equation}{ D^T D A = \lambda \left( C\right) A \f} The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients \f{equation*}{ A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \f} The scaling factor guarantees that \f$A^T C A =1\f$.

Positional Arguments

• points: UMat.
• distType: int.
• param: double.
• reps: double.
• aeps: double.

Keyword Arguments

• line: UMat.

Fits a line to a 2D or 3D point set. The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where \f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one of the following

Positional Arguments

• points: UMat.
• distType: int.
• param: double.
• reps: double.
• aeps: double.

Keyword Arguments

• line: UMat.

Fits a line to a 2D or 3D point set. The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where \f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one of the following

Positional Arguments

• src: UMat.
• flipCode: int.

Keyword Arguments

• dst: UMat.

Flips a 2D array around vertical, horizontal, or both axes. The function cv::flip flips the array in one of three different ways (row and column indices are 0-based): \f[\texttt{dst} _{ij} = \left\{ \begin{array}{l l} \texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ \texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ \texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ \end{array} \right.\f] The example scenarios of using the function are the following

Positional Arguments

• src: UMat.
• flipCode: int.

Keyword Arguments

• dst: UMat.

Flips a 2D array around vertical, horizontal, or both axes. The function cv::flip flips the array in one of three different ways (row and column indices are 0-based): \f[\texttt{dst} _{ij} = \left\{ \begin{array}{l l} \texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ \texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ \texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ \end{array} \right.\f] The example scenarios of using the function are the following

Positional Arguments

• image: UMat.
• mask: UMat.
• seedPoint: Point.
• newVal: Scalar.

Keyword Arguments

• loDiff: Scalar.
• upDiff: Scalar.
• flags: int.

Fills a connected component with the given color. The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if

Positional Arguments

• image: UMat.
• mask: UMat.
• seedPoint: Point.
• newVal: Scalar.

Keyword Arguments

• loDiff: Scalar.
• upDiff: Scalar.
• flags: int.

Fills a connected component with the given color. The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if

Positional Arguments

• src: UMat.
• ksize: Size.
• sigmaX: double.

Keyword Arguments

• dst: UMat.
• sigmaY: double.
• borderType: int.

Blurs an image using a Gaussian filter. The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

Positional Arguments

• src: UMat.
• ksize: Size.
• sigmaX: double.

Keyword Arguments

• dst: UMat.
• sigmaY: double.
• borderType: int.

Blurs an image using a Gaussian filter. The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

Positional Arguments

• src1: UMat.
• src2: UMat.
• alpha: double.
• src3: UMat.
• beta: double.

Keyword Arguments

• dst: UMat.
• flags: int.

Performs generalized matrix multiplication. The function cv::gemm performs generalized matrix multiplication similar to the gemm functions in BLAS level 3. For example, gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T) corresponds to \f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] In case of complex (two-channel) data, performed a complex matrix multiplication. The function can be replaced with a matrix expression. For example, the above call can be replaced with

Positional Arguments

• src1: UMat.
• src2: UMat.
• alpha: double.
• src3: UMat.
• beta: double.

Keyword Arguments

• dst: UMat.
• flags: int.

Performs generalized matrix multiplication. The function cv::gemm performs generalized matrix multiplication similar to the gemm functions in BLAS level 3. For example, gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T) corresponds to \f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] In case of complex (two-channel) data, performed a complex matrix multiplication. The function can be replaced with a matrix expression. For example, the above call can be replaced with

Has overloading in C++

Returns full configuration time cmake output. Returned value is raw cmake output including version control system revision, compiler version, compiler flags, enabled modules and third party libraries, etc. Output format depends on target architecture.

Returns list of CPU features enabled during compilation. Returned value is a string containing space separated list of CPU features with following markers

Returns the number of CPU ticks. The function returns the current number of CPU ticks on some architectures (such as x86, x64, PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU with its own counter. So, theoretically (and practically) the subsequent calls to the function do not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU frequency depending on the load, the number of CPU clocks spent in some code cannot be directly converted to time units. Therefore, getTickCount is generally a preferable solution for measuring execution time.

Positional Arguments

• cameraMatrix: UMat.

Keyword Arguments

• imgsize: Size.
• centerPrincipalPoint: bool.

Returns the default new camera matrix. The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). In the latter case, the new camera matrix will be: \f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Positional Arguments

• cameraMatrix: UMat.

Keyword Arguments

• imgsize: Size.
• centerPrincipalPoint: bool.

Returns the default new camera matrix. The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). In the latter case, the new camera matrix will be: \f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Positional Arguments

• dx: int.
• dy: int.
• ksize: int.

Keyword Arguments

• kx: UMat.
• ky: UMat.
• normalize: bool.
• ktype: int.

Returns filter coefficients for computing spatial image derivatives. The function computes and returns the filter coefficients for spatial image derivatives. When ksize=FILTER_SCHARR, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to

Positional Arguments

• dx: int.
• dy: int.
• ksize: int.

Keyword Arguments

• kx: UMat.
• ky: UMat.
• normalize: bool.
• ktype: int.

Returns filter coefficients for computing spatial image derivatives. The function computes and returns the filter coefficients for spatial image derivatives. When ksize=FILTER_SCHARR, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to

Positional Arguments

• fontFace: int.
• pixelHeight: int.

Keyword Arguments

• thickness: int.

Calculates the font-specific size to use to achieve a given height in pixels. @return The fontSize to use for cv::putText @see cv::putText

Positional Arguments

• fontFace: int.
• pixelHeight: int.

Keyword Arguments

• thickness: int.

Calculates the font-specific size to use to achieve a given height in pixels. @return The fontSize to use for cv::putText @see cv::putText

Positional Arguments

• ksize: Size.
• sigma: double.
• theta: double.
• lambd: double.
• gamma: double.

Keyword Arguments

• psi: double.
• ktype: int.

Returns Gabor filter coefficients. For more details about gabor filter equations and parameters, see: Gabor Filter.

Positional Arguments

• ksize: Size.
• sigma: double.
• theta: double.
• lambd: double.
• gamma: double.

Keyword Arguments

• psi: double.
• ktype: int.

Returns Gabor filter coefficients. For more details about gabor filter equations and parameters, see: Gabor Filter.

Positional Arguments

• ksize: int.
• sigma: double.

Keyword Arguments

• ktype: int.

Returns Gaussian filter coefficients. The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter coefficients: \f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.

Positional Arguments

• ksize: int.
• sigma: double.

Keyword Arguments

• ktype: int.

Returns Gaussian filter coefficients. The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter coefficients: \f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.

Returns feature name by ID Returns empty string if feature is not defined

Python prototype (for reference):

Returns the number of logical CPUs available for the process.

Returns the number of threads used by OpenCV for parallel regions. Always returns 1 if OpenCV is built without threading support. The exact meaning of return value depends on the threading framework used by OpenCV library

Returns the optimal DFT size for a given vector size. DFT performance is not a monotonic function of a vector size. Therefore, when you calculate convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to pad the input data with zeros to get a bit larger array that can be transformed much faster than the original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5*5*3*2*2) are also processed quite efficiently. The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize so that the DFT of a vector of size N can be processed efficiently. In the current implementation N = 2 ^p^ * 3 ^q^ * 5 ^r^ for some integer p, q, r. The function returns a negative number if vecsize is too large (very close to INT_MAX ). While the function cannot be used directly to estimate the optimal vector size for DCT transform (since the current DCT implementation supports only even-size vectors), it can be easily processed as getOptimalDFTSize((vecsize+1)/2)*2. @sa dft , dct , idft , idct , mulSpectrums

Positional Arguments

• cameraMatrix: UMat.
• distCoeffs: UMat.
• imageSize: Size.
• alpha: double.

Keyword Arguments

• newImgSize: Size.
• centerPrincipalPoint: bool.

Returns the new camera intrinsic matrix based on the free scaling parameter.

Positional Arguments

• cameraMatrix: UMat.
• distCoeffs: UMat.
• imageSize: Size.
• alpha: double.

Keyword Arguments

• newImgSize: Size.
• centerPrincipalPoint: bool.

Returns the new camera intrinsic matrix based on the free scaling parameter.

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• solveMethod: int.

Calculates a perspective transform from four pairs of the corresponding points. The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: \f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] where \f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] @sa findHomography, warpPerspective, perspectiveTransform

Positional Arguments

• src: UMat.
• dst: UMat.

Keyword Arguments

• solveMethod: int.

Calculates a perspective transform from four pairs of the corresponding points. The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: \f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] where \f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] @sa findHomography, warpPerspective, perspectiveTransform

Positional Arguments

• image: UMat.
• patchSize: Size.
• center: Point2f.

Keyword Arguments

• patch: UMat.
• patchType: int.

Retrieves a pixel rectangle from an image with sub-pixel accuracy. The function getRectSubPix extracts pixels from src: \f[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] where the values of the pixels at non-integer coordinates are retrieved using bilinear interpolation. Every channel of multi-channel images is processed independently. Also the image should be a single channel or three channel image. While the center of the rectangle must be inside the image, parts of the rectangle may be outside.

Positional Arguments

• image: UMat.
• patchSize: Size.
• center: Point2f.

Keyword Arguments

• patch: UMat.
• patchType: int.

Retrieves a pixel rectangle from an image with sub-pixel accuracy. The function getRectSubPix extracts pixels from src: \f[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] where the values of the pixels at non-integer coordinates are retrieved using bilinear interpolation. Every channel of multi-channel images is processed independently. Also the image should be a single channel or three channel image. While the center of the rectangle must be inside the image, parts of the rectangle may be outside.

Calculates an affine matrix of 2D rotation. The function calculates the following matrix: \f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f] where \f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f] The transformation maps the rotation center to itself. If this is not the target, adjust the shift.

Positional Arguments

• shape: int.
• ksize: Size.

Keyword Arguments

• anchor: Point.

Returns a structuring element of the specified size and shape for morphological operations. The function constructs and returns the structuring element that can be further passed to #erode, #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as the structuring element.

Positional Arguments

• shape: int.
• ksize: Size.

Keyword Arguments

• anchor: Point.

Returns a structuring element of the specified size and shape for morphological operations. The function constructs and returns the structuring element that can be further passed to #erode, #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as the structuring element.

Calculates the width and height of a text string. The function cv::getTextSize calculates and returns the size of a box that contains the specified text. That is, the following code renders some text, the tight box surrounding it, and the baseline:

Returns the index of the currently executed thread within the current parallel region. Always returns 0 if called outside of parallel region. @deprecated Current implementation doesn't corresponding to this documentation. The exact meaning of the return value depends on the threading framework used by OpenCV library

Returns the number of ticks. The function returns the number of ticks after the certain event (for example, when the machine was turned on). It can be used to initialize RNG or to measure a function execution time by reading the tick count before and after the function call. @sa getTickFrequency, TickMeter

Returns the number of ticks per second. The function returns the number of ticks per second. That is, the following code computes the execution time in seconds

Returns the trackbar position. The function returns the current position of the specified trackbar. Note: [Qt Backend Only] winname can be empty if the trackbar is attached to the control panel.

Python prototype (for reference):

Returns major library version

Returns minor library version

Returns revision field of the library version

Returns library version string For example "3.4.1-dev". @sa getMajorVersion, getMinorVersion, getRevisionVersion

Provides rectangle of image in the window. The function getWindowImageRect returns the client screen coordinates, width and height of the image rendering area. @sa resizeWindow moveWindow

Provides parameters of a window. The function getWindowProperty returns properties of a window. @sa setWindowProperty

Positional Arguments

• image: UMat.
• maxCorners: int.
• qualityLevel: double.
• minDistance: double.

Keyword Arguments

• corners: UMat.
• mask: UMat.
• blockSize: int.
• useHarrisDetector: bool.
• k: double.

Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in @cite Shi94

Positional Arguments

• image: UMat.
• maxCorners: int.
• qualityLevel: double.
• minDistance: double.

Keyword Arguments

• corners: UMat.
• mask: UMat.
• blockSize: int.
• useHarrisDetector: bool.
• k: double.

Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in @cite Shi94

Positional Arguments

• image: UMat
• maxCorners: int
• qualityLevel: double
• minDistance: double
• mask: UMat
• blockSize: int
• gradientSize: int

Keyword Arguments

• corners: UMat.
• useHarrisDetector: bool.
• k: double.

Python prototype (for reference):

Positional Arguments

• image: UMat
• maxCorners: int
• qualityLevel: double
• minDistance: double
• mask: UMat
• blockSize: int
• gradientSize: int

Keyword Arguments

• corners: UMat.
• useHarrisDetector: bool.
• k: double.

Python prototype (for reference):

Positional Arguments

• image: UMat.
• maxCorners: int.
• qualityLevel: double.
• minDistance: double.
• mask: UMat.

Keyword Arguments

• corners: UMat.
• cornersQuality: UMat.
• blockSize: int.
• gradientSize: int.
• useHarrisDetector: bool.
• k: double.

Same as above, but returns also quality measure of the detected corners.

Positional Arguments

• image: UMat.
• maxCorners: int.
• qualityLevel: double.
• minDistance: double.
• mask: UMat.

Keyword Arguments

• corners: UMat.
• cornersQuality: UMat.
• blockSize: int.
• gradientSize: int.
• useHarrisDetector: bool.
• k: double.

Same as above, but returns also quality measure of the detected corners.

Positional Arguments

• img: UMat.
• mask: UMat.
• rect: Rect.
• bgdModel: UMat.
• fgdModel: UMat.
• iterCount: int.

Keyword Arguments

• mode: int.

Runs the GrabCut algorithm. The function implements the GrabCut image segmentation algorithm.

Positional Arguments

• img: UMat.
• mask: UMat.
• rect: Rect.
• bgdModel: UMat.
• fgdModel: UMat.
• iterCount: int.

Keyword Arguments

• mode: int.

Runs the GrabCut algorithm. The function implements the GrabCut image segmentation algorithm.

Positional Arguments

• rectList: vector_Rect
• groupThreshold: int

Keyword Arguments

• eps: double.

Has overloading in C++

Positional Arguments

• rectList: vector_Rect
• groupThreshold: int

Keyword Arguments

• eps: double.

Has overloading in C++

Returns true if the specified image can be decoded by OpenCV

Returns true if an image with the specified filename can be encoded by OpenCV

Python prototype (for reference):

Positional Arguments

• src: vector_UMat.

Keyword Arguments

• dst: UMat.

Has overloading in C++

Positional Arguments

• src: vector_UMat.

Keyword Arguments

• dst: UMat.

Has overloading in C++

Positional Arguments

• image: UMat.
• method: int.
• dp: double.
• minDist: double.

Keyword Arguments

• circles: UMat.
• param1: double.
• param2: double.
• minRadius: int.
• maxRadius: int.

Finds circles in a grayscale image using the Hough transform. The function finds circles in a grayscale image using a modification of the Hough transform. Example: : @include snippets/imgproc_HoughLinesCircles.cpp Note: Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure. It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.

Positional Arguments

• image: UMat.
• method: int.
• dp: double.
• minDist: double.

Keyword Arguments

• circles: UMat.
• param1: double.
• param2: double.
• minRadius: int.
• maxRadius: int.

Finds circles in a grayscale image using the Hough transform. The function finds circles in a grayscale image using a modification of the Hough transform. Example: : @include snippets/imgproc_HoughLinesCircles.cpp Note: Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure. It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.

Positional Arguments

• image: UMat.
• rho: double.
• theta: double.
• threshold: int.

Keyword Arguments

• lines: UMat.
• srn: double.
• stn: double.
• min_theta: double.
• max_theta: double.

Finds lines in a binary image using the standard Hough transform. The function implements the standard or standard multi-scale Hough transform algorithm for line detection. See http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm for a good explanation of Hough transform.

Positional Arguments

• image: UMat.
• rho: double.
• theta: double.
• threshold: int.

Keyword Arguments

• lines: UMat.
• srn: double.
• stn: double.
• min_theta: double.
• max_theta: double.

Finds lines in a binary image using the standard Hough transform. The function implements the standard or standard multi-scale Hough transform algorithm for line detection. See http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm for a good explanation of Hough transform.

Positional Arguments

• image: UMat.
• rho: double.
• theta: double.
• threshold: int.

Keyword Arguments

• lines: UMat.
• minLineLength: double.
• maxLineGap: double.

Finds line segments in a binary image using the probabilistic Hough transform. The function implements the probabilistic Hough transform algorithm for line detection, described in @cite Matas00 See the line detection example below: @include snippets/imgproc_HoughLinesP.cpp This is a sample picture the function parameters have been tuned for: And this is the output of the above program in case of the probabilistic Hough transform:

Positional Arguments

• image: UMat.
• rho: double.
• theta: double.
• threshold: int.

Keyword Arguments

• lines: UMat.
• minLineLength: double.
• maxLineGap: double.

Finds line segments in a binary image using the probabilistic Hough transform. The function implements the probabilistic Hough transform algorithm for line detection, described in @cite Matas00 See the line detection example below: @include snippets/imgproc_HoughLinesP.cpp This is a sample picture the function parameters have been tuned for: And this is the output of the above program in case of the probabilistic Hough transform:

Positional Arguments

• point: UMat.
• lines_max: int.
• threshold: int.
• min_rho: double.
• max_rho: double.
• rho_step: double.
• min_theta: double.
• max_theta: double.
• theta_step: double.

Keyword Arguments

• lines: UMat.

Finds lines in a set of points using the standard Hough transform. The function finds lines in a set of points using a modification of the Hough transform. @include snippets/imgproc_HoughLinesPointSet.cpp

Positional Arguments

• point: UMat.
• lines_max: int.
• threshold: int.
• min_rho: double.
• max_rho: double.
• rho_step: double.
• min_theta: double.
• max_theta: double.
• theta_step: double.

Keyword Arguments

• lines: UMat.

Finds lines in a set of points using the standard Hough transform. The function finds lines in a set of points using a modification of the Hough transform. @include snippets/imgproc_HoughLinesPointSet.cpp

Positional Arguments

• image: UMat
• rho: double
• theta: double
• threshold: int

Keyword Arguments

• lines: UMat.
• srn: double.
• stn: double.
• min_theta: double.
• max_theta: double.

Finds lines in a binary image using the standard Hough transform and get accumulator. Note: This function is for bindings use only. Use original function in C++ code @sa HoughLines

Positional Arguments

• image: UMat
• rho: double
• theta: double
• threshold: int

Keyword Arguments

• lines: UMat.
• srn: double.
• stn: double.
• min_theta: double.
• max_theta: double.

Finds lines in a binary image using the standard Hough transform and get accumulator. Note: This function is for bindings use only. Use original function in C++ code @sa HoughLines

Positional Arguments

• m: Moments

Keyword Arguments

• hu: UMat.

Has overloading in C++

Positional Arguments

• m: Moments

Keyword Arguments

• hu: UMat.

Has overloading in C++

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.

Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). @sa dct, dft, idft, getOptimalDFTSize

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.

Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). @sa dct, dft, idft, getOptimalDFTSize

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.
• nonzeroRows: int.

Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . Note: None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of dft or idft explicitly to make these transforms mutually inverse. @sa dft, dct, idct, mulSpectrums, getOptimalDFTSize

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.
• nonzeroRows: int.

Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . Note: None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of dft or idft explicitly to make these transforms mutually inverse. @sa dft, dct, idct, mulSpectrums, getOptimalDFTSize

Positional Arguments

• src: UMat.
• mask: UMat.

Keyword Arguments

• dst: UMat.
• alpha: float.
• beta: float.

Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. This is useful to highlight under-exposed foreground objects or to reduce specular reflections.

Positional Arguments

• src: UMat.
• mask: UMat.

Keyword Arguments

• dst: UMat.
• alpha: float.
• beta: float.

Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. This is useful to highlight under-exposed foreground objects or to reduce specular reflections.

Positional Arguments

• filename: String.

Keyword Arguments

• flags: int.

Returns the number of images inside the give file The function imcount will return the number of pages in a multi-page image, or 1 for single-page images

Positional Arguments

• filename: String.

Keyword Arguments

• flags: int.

Returns the number of images inside the give file The function imcount will return the number of pages in a multi-page image, or 1 for single-page images

Positional Arguments

• ext: String.
• img: UMat.

Keyword Arguments

• params: vector_int.

Encodes an image into a memory buffer. The function imencode compresses the image and stores it in the memory buffer that is resized to fit the result. See cv::imwrite for the list of supported formats and flags description.

Positional Arguments

• ext: String.
• img: UMat.

Keyword Arguments

• params: vector_int.

Encodes an image into a memory buffer. The function imencode compresses the image and stores it in the memory buffer that is resized to fit the result. See cv::imwrite for the list of supported formats and flags description.

Positional Arguments

• filename: String.

Keyword Arguments

• flags: int.

Loads an image from a file. @anchor imread The function imread loads an image from the specified file and returns it. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format), the function returns an empty matrix ( Mat::data==NULL ). Currently, the following file formats are supported

Positional Arguments

• filename: String.

Keyword Arguments

• flags: int.

Loads an image from a file. @anchor imread The function imread loads an image from the specified file and returns it. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format), the function returns an empty matrix ( Mat::data==NULL ). Currently, the following file formats are supported

Positional Arguments

• filename: String.

Keyword Arguments

• mats: vector_Mat.
• flags: int.

Loads a multi-page image from a file. The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. @sa cv::imread

Positional Arguments

• filename: String.

Keyword Arguments

• mats: vector_Mat.
• flags: int.

Loads a multi-page image from a file. The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. @sa cv::imread

Positional Arguments

• filename: String.
• start: int.
• count: int.

Keyword Arguments

• mats: vector_Mat.
• flags: int.

Loads a of images of a multi-page image from a file. The function imreadmulti loads a specified range from a multi-page image from the specified file into a vector of Mat objects. @sa cv::imread

Positional Arguments

• filename: String.
• start: int.
• count: int.

Keyword Arguments

• mats: vector_Mat.
• flags: int.

Loads a of images of a multi-page image from a file. The function imreadmulti loads a specified range from a multi-page image from the specified file into a vector of Mat objects. @sa cv::imread

Positional Arguments

• filename: String.
• img: UMat.

Keyword Arguments

• params: vector_int.

Saves an image to a specified file. The function imwrite saves the image to the specified file. The image format is chosen based on the filename extension (see cv::imread for the list of extensions). In general, only 8-bit single-channel or 3-channel (with 'BGR' channel order) images can be saved using this function, with these exceptions

Positional Arguments

• filename: String.
• img: UMat.

Keyword Arguments

• params: vector_int.

Saves an image to a specified file. The function imwrite saves the image to the specified file. The image format is chosen based on the filename extension (see cv::imread for the list of extensions). In general, only 8-bit single-channel or 3-channel (with 'BGR' channel order) images can be saved using this function, with these exceptions

Positional Arguments

• filename: String
• img: vector_UMat

Keyword Arguments

• params: vector_int.

Python prototype (for reference):

Positional Arguments

• filename: String
• img: vector_UMat

Keyword Arguments

• params: vector_int.

Python prototype (for reference):

Positional Arguments

• objectPoints: vector_UMat.
• imagePoints: vector_UMat.
• imageSize: Size.

Keyword Arguments

• aspectRatio: double.

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Positional Arguments

• objectPoints: vector_UMat.
• imagePoints: vector_UMat.
• imageSize: Size.

Keyword Arguments

• aspectRatio: double.

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Positional Arguments

• cameraMatrix: UMat.
• distCoeffs: UMat.
• r: UMat.
• newCameraMatrix: UMat.
• size: Size.
• m1type: int.

Keyword Arguments

• map1: UMat.
• map2: UMat.

Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of #initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs. The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for #remap. The projected image looks like a distorted version of the original which, once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs, this helps align the projector (in the same manner as #initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair). The function builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \f$(u, v)\f$ in the destination (projected and inverse-rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied: \f[ \begin{array}{l} \text{newCameraMatrix}\\ x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ \\\text{Undistortion} \\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\ r^2 \leftarrow x^2 + y^2 \\ \theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\ x' \leftarrow \frac{x}{\theta} \\ y' \leftarrow \frac{y}{\theta} \\ \\\text{Rectification}\\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x'' \leftarrow X/W \\ y'' \leftarrow Y/W \\ \\\text{cameraMatrix}\\ map_x(u,v) \leftarrow x'' f_x + c_x \\ map_y(u,v) \leftarrow y'' f_y + c_y \end{array} \f] where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ are the distortion coefficients vector distCoeffs. In case of a stereo-rectified projector-camera pair, this function is called for the projector while #initUndistortRectifyMap is called for the camera head. This is done after #stereoRectify, which in turn is called after #stereoCalibrate. If the projector-camera pair is not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For the projector and camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as \f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] where cameraMatrix can be chosen arbitrarily.

Positional Arguments

• cameraMatrix: UMat.
• distCoeffs: UMat.
• r: UMat.
• newCameraMatrix: UMat.
• size: Size.
• m1type: int.

Keyword Arguments

• map1: UMat.
• map2: UMat.

Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of #initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs. The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for #remap. The projected image looks like a distorted version of the original which, once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs, this helps align the projector (in the same manner as #initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair). The function builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \f$(u, v)\f$ in the destination (projected and inverse-rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied: \f[ \begin{array}{l} \text{newCameraMatrix}\\ x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ \\\text{Undistortion} \\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\ r^2 \leftarrow x^2 + y^2 \\ \theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\ x' \leftarrow \frac{x}{\theta} \\ y' \leftarrow \frac{y}{\theta} \\ \\\text{Rectification}\\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x'' \leftarrow X/W \\ y'' \leftarrow Y/W \\ \\\text{cameraMatrix}\\ map_x(u,v) \leftarrow x'' f_x + c_x \\ map_y(u,v) \leftarrow y'' f_y + c_y \end{array} \f] where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ are the distortion coefficients vector distCoeffs. In case of a stereo-rectified projector-camera pair, this function is called for the projector while #initUndistortRectifyMap is called for the camera head. This is done after #stereoRectify, which in turn is called after #stereoCalibrate. If the projector-camera pair is not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For the projector and camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as \f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] where cameraMatrix can be chosen arbitrarily.

Positional Arguments

• cameraMatrix: UMat.
• distCoeffs: UMat.
• r: UMat.
• newCameraMatrix: UMat.
• size: Size.
• m1type: int.

Keyword Arguments

• map1: UMat.
• map2: UMat.

Computes the undistortion and rectification transformation map. The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for #remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied: \f[ \begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ r^2 \leftarrow x'^2 + y'^2 \\ x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ map_x(u,v) \leftarrow x''' f_x + c_x \\ map_y(u,v) \leftarrow y''' f_y + c_y \end{array} \f] where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ are the distortion coefficients. In case of a stereo camera, this function is called twice: once for each camera head, after #stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as \f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] where cameraMatrix can be chosen arbitrarily.

Positional Arguments

• cameraMatrix: UMat.
• distCoeffs: UMat.
• r: UMat.
• newCameraMatrix: UMat.
• size: Size.
• m1type: int.

Keyword Arguments

• map1: UMat.
• map2: UMat.

Computes the undistortion and rectification transformation map. The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for #remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied: \f[ \begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ r^2 \leftarrow x'^2 + y'^2 \\ x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ map_x(u,v) \leftarrow x''' f_x + c_x \\ map_y(u,v) \leftarrow y''' f_y + c_y \end{array} \f] where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ are the distortion coefficients. In case of a stereo camera, this function is called twice: once for each camera head, after #stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as \f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] where cameraMatrix can be chosen arbitrarily.

Positional Arguments

• src: UMat.
• inpaintMask: UMat.
• inpaintRadius: double.
• flags: int.

Keyword Arguments

• dst: UMat.

Restores the selected region in an image using the region neighborhood.

Positional Arguments

• src: UMat.
• inpaintMask: UMat.
• inpaintRadius: double.
• flags: int.

Keyword Arguments

• dst: UMat.

Restores the selected region in an image using the region neighborhood.

Positional Arguments

• src: UMat.
• lowerb: UMat.
• upperb: UMat.

Keyword Arguments

• dst: UMat.

Checks if array elements lie between the elements of two other arrays. The function checks the range as follows

Positional Arguments

• src: UMat.
• lowerb: UMat.
• upperb: UMat.

Keyword Arguments

• dst: UMat.

Checks if array elements lie between the elements of two other arrays. The function checks the range as follows

Inserts a single channel to dst (coi is 0-based index) @sa mixChannels, merge

Positional Arguments

• src: UMat

Keyword Arguments

• sum: UMat.
• sqsum: UMat.
• sdepth: int.
• sqdepth: int.

Has overloading in C++

Positional Arguments

• src: UMat

Keyword Arguments

• sum: UMat.
• sqsum: UMat.
• sdepth: int.
• sqdepth: int.

Has overloading in C++

Positional Arguments

• src: UMat.

Keyword Arguments

• sum: UMat.
• sqsum: UMat.
• tilted: UMat.
• sdepth: int.
• sqdepth: int.

Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \f[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\f] \f[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\f] \f[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\f] Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example: \f[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\f] It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted .

Positional Arguments

• src: UMat.

Keyword Arguments

• sum: UMat.
• sqsum: UMat.
• tilted: UMat.
• sdepth: int.
• sqdepth: int.

Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \f[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\f] \f[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\f] \f[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\f] Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example: \f[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\f] It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted .

Positional Arguments

• src: UMat

Keyword Arguments

• sum: UMat.
• sdepth: int.

Has overloading in C++

Positional Arguments

• src: UMat

Keyword Arguments

• sum: UMat.
• sdepth: int.

Has overloading in C++

Positional Arguments

• p1: UMat.
• p2: UMat.

Keyword Arguments

• p12: UMat.
• handleNested: bool.

Finds intersection of two convex polygons

Positional Arguments

• p1: UMat.
• p2: UMat.

Keyword Arguments

• p12: UMat.
• handleNested: bool.

Finds intersection of two convex polygons

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.

Finds the inverse or pseudo-inverse of a matrix. The function cv::invert inverts the matrix src and stores the result in dst . When the matrix src is singular or non-square, the function calculates the pseudo-inverse matrix (the dst matrix) so that norm(src*dst - I) is minimal, where I is an identity matrix. In case of the #DECOMP_LU method, the function returns non-zero value if the inverse has been successfully calculated and 0 if src is singular. In case of the #DECOMP_SVD method, the function returns the inverse condition number of src (the ratio of the smallest singular value to the largest singular value) and 0 if src is singular. The SVD method calculates a pseudo-inverse matrix if src is singular. Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with non-singular square matrices that should also be symmetrical and positively defined. In this case, the function stores the inverted matrix in dst and returns non-zero. Otherwise, it returns 0. @sa solve, SVD

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.
• flags: int.

Finds the inverse or pseudo-inverse of a matrix. The function cv::invert inverts the matrix src and stores the result in dst . When the matrix src is singular or non-square, the function calculates the pseudo-inverse matrix (the dst matrix) so that norm(src*dst - I) is minimal, where I is an identity matrix. In case of the #DECOMP_LU method, the function returns non-zero value if the inverse has been successfully calculated and 0 if src is singular. In case of the #DECOMP_SVD method, the function returns the inverse condition number of src (the ratio of the smallest singular value to the largest singular value) and 0 if src is singular. The SVD method calculates a pseudo-inverse matrix if src is singular. Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with non-singular square matrices that should also be symmetrical and positively defined. In this case, the function stores the inverted matrix in dst and returns non-zero. Otherwise, it returns 0. @sa solve, SVD

Positional Arguments

• m: UMat.

Keyword Arguments

• iM: UMat.

Inverts an affine transformation. The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: \f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] The result is also a \f$2 \times 3\f$ matrix of the same type as M.

Positional Arguments

• m: UMat.

Keyword Arguments

• iM: UMat.

Inverts an affine transformation. The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: \f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] The result is also a \f$2 \times 3\f$ matrix of the same type as M.

Tests a contour convexity. The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.

Positional Arguments

• data: UMat.
• k: int.
• bestLabels: UMat.
• criteria: TermCriteria.
• attempts: int.
• flags: int.

Keyword Arguments

• centers: UMat.

Finds centers of clusters and groups input samples around the clusters. The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a 0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. Note:

Positional Arguments

• data: UMat.
• k: int.
• bestLabels: UMat.
• criteria: TermCriteria.
• attempts: int.
• flags: int.

Keyword Arguments

• centers: UMat.

Finds centers of clusters and groups input samples around the clusters. The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a 0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. Note:

Positional Arguments

• src: UMat.
• ddepth: int.

Keyword Arguments

• dst: UMat.
• ksize: int.
• scale: double.
• delta: double.
• borderType: int.

Calculates the Laplacian of an image. The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator: \f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \f$3 \times 3\f$ aperture: \f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]

Positional Arguments

• src: UMat.
• ddepth: int.

Keyword Arguments

• dst: UMat.
• ksize: int.
• scale: double.
• delta: double.
• borderType: int.

Calculates the Laplacian of an image. The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator: \f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \f$3 \times 3\f$ aperture: \f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]

Positional Arguments

• img: UMat. Image.
• pt1: Point.
• pt2: Point.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• shift: int.

Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.

Positional Arguments

• img: UMat. Image.
• pt1: Point.
• pt2: Point.
• color: Scalar.

Keyword Arguments

• thickness: int.
• lineType: int.
• shift: int.

Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.

Positional Arguments

• src: UMat.
• center: Point2f.
• maxRadius: double.
• flags: int.

Keyword Arguments

• dst: UMat.

Remaps an image to polar coordinates space. @deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) @internal Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"): \f[\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\f] where \f[\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ \phi = angle \cdot \texttt{angle} (I) \end{array}\f] and \f[\begin{array}{l} Kx = src.cols / maxRadius \\ Ky = src.rows / 2\Pi \end{array}\f] Note:

Positional Arguments

• src: UMat.
• center: Point2f.
• maxRadius: double.
• flags: int.

Keyword Arguments

• dst: UMat.

Remaps an image to polar coordinates space. @deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) @internal Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"): \f[\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\f] where \f[\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ \phi = angle \cdot \texttt{angle} (I) \end{array}\f] and \f[\begin{array}{l} Kx = src.cols / maxRadius \\ Ky = src.rows / 2\Pi \end{array}\f] Note:

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Calculates the natural logarithm of every array element. The function cv::log calculates the natural logarithm of every element of the input array: \f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f] Output on zero, negative and special (NaN, Inf) values is undefined. @sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude

Positional Arguments

• src: UMat.

Keyword Arguments

• dst: UMat.

Calculates the natural logarithm of every array element. The function cv::log calculates the natural logarithm of every element of the input array: \f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f] Output on zero, negative and special (NaN, Inf) values is undefined. @sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude

Positional Arguments

• src: UMat.
• center: Point2f.
• m: double.
• flags: int.

Keyword Arguments

• dst: UMat.

Remaps an image to semilog-polar coordinates space. @deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); @internal Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"): \f[\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\f] where \f[\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\f] and \f[\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\f] The function emulates the human "foveal" vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth. Note:

Positional Arguments

• src: UMat.
• center: Point2f.
• m: double.
• flags: int.

Keyword Arguments

• dst: UMat.

Remaps an image to semilog-polar coordinates space. @deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); @internal Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"): \f[\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\f] where \f[\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\f] and \f[\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\f] The function emulates the human "foveal" vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth. Note:

Positional Arguments

• src: UMat.
• lut: UMat.

Keyword Arguments

• dst: UMat.

Performs a look-up table transform of an array. The function LUT fills the output array with values from the look-up table. Indices of the entries are taken from the input array. That is, the function processes each element of src as follows: \f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] where \f[d = \fork{0}{if (\texttt{src}) has depth (\texttt{CV_8U})}{128}{if (\texttt{src}) has depth (\texttt{CV_8S})}\f]

Positional Arguments

• src: UMat.
• lut: UMat.

Keyword Arguments

• dst: UMat.

Performs a look-up table transform of an array. The function LUT fills the output array with values from the look-up table. Indices of the entries are taken from the input array. That is, the function processes each element of src as follows: \f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] where \f[d = \fork{0}{if (\texttt{src}) has depth (\texttt{CV_8U})}{128}{if (\texttt{src}) has depth (\texttt{CV_8S})}\f]

Positional Arguments

• x: UMat.
• y: UMat.

Keyword Arguments

• magnitude: UMat.

Calculates the magnitude of 2D vectors. The function cv::magnitude calculates the magnitude of 2D vectors formed from the corresponding elements of x and y arrays: \f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f]

Positional Arguments

• x: UMat.
• y: UMat.

Keyword Arguments

• magnitude: UMat.

Calculates the magnitude of 2D vectors. The function cv::magnitude calculates the magnitude of 2D vectors formed from the corresponding elements of x and y arrays: \f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f]

Calculates the Mahalanobis distance between two vectors. The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: \f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f] The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using the invert function (preferably using the #DECOMP_SVD method, as the most accurate).

Compares two shapes. The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments)

Positional Arguments

• image: UMat.
• templ: UMat.
• method: int.

Keyword Arguments

• result: UMat.
• mask: UMat.

Compares a template against overlapped image regions. The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against templ using the specified method and stores the comparison results in result . #TemplateMatchModes describes the formulae for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result, \f$M\f$ the optional mask ). The summation is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ After the function finishes the comparison, the best matches can be found as global minimums (when #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.

Positional Arguments

• image: UMat.
• templ: UMat.
• method: int.

Keyword Arguments

• result: UMat.
• mask: UMat.

Compares a template against overlapped image regions. The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against templ using the specified method and stores the comparison results in result . #TemplateMatchModes describes the formulae for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result, \f$M\f$ the optional mask ). The summation is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ After the function finishes the comparison, the best matches can be found as global minimums (when #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.

Positional Arguments

• a: UMat.
• b: UMat.

Keyword Arguments

• dABdA: UMat.
• dABdB: UMat.

Computes partial derivatives of the matrix product for each multiplied matrix.

Positional Arguments

• a: UMat.
• b: UMat.

Keyword Arguments

• dABdA: UMat.
• dABdB: UMat.

Computes partial derivatives of the matrix product for each multiplied matrix.

Positional Arguments

• src1: UMat.
• src2: UMat.

Keyword Arguments

• dst: UMat.

Calculates per-element maximum of two arrays or an array and a scalar. The function cv::max calculates the per-element maximum of two arrays: \f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] or array and a scalar: \f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] @sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions

Positional Arguments

• src1: UMat.
• src2: UMat.

Keyword Arguments

• dst: UMat.

Calculates per-element maximum of two arrays or an array and a scalar. The function cv::max calculates the per-element maximum of two arrays: \f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] or array and a scalar: \f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] @sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions

Positional Arguments

• src: UMat.

Keyword Arguments

• mask: UMat.

Calculates an average (mean) of array elements. The function cv::mean calculates the mean value M of array elements, independently for each channel, and return it: \f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] When all the mask elements are 0's, the function returns Scalar::all(0)

Positional Arguments

• src: UMat.

Keyword Arguments

• mask: UMat.

Calculates an average (mean) of array elements. The function cv::mean calculates the mean value M of array elements, independently for each channel, and return it: \f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] When all the mask elements are 0's, the function returns Scalar::all(0)

Finds an object on a back projection image.

Positional Arguments

• src: UMat.